{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 1 Introduction \n",
    "* 2 Load and check data\n",
    "    * 2.1 load data \n",
    "    * 2.2 Outlier detectio \n",
    "    * 2.3 join train and test set \n",
    "    * check for null and missing values \n",
    "* 3 Feature analysis \n",
    "    * 3.1 Numerical values \n",
    "    * 3.2 Categorical values \n",
    "* 4 Filling missing Values \n",
    "    * 4.1 Age \n",
    "* 5 Feature engineering \n",
    "    * 5.1 Name/Title \n",
    "    * 5.2 Family Size \n",
    "    * Cabin \n",
    "    * Ticket \n",
    "* Modeling \n",
    "    * 6.1 Simple modeling \n",
    "        * 6.1.1 Cross validate models \n",
    "        * 6.1.2 Hyperparmater tunning for best models \n",
    "        * 6.1.3 Plot learning curves \n",
    "        * 6.1.4 Feature importance of the tree based classifiers \n",
    "    * 6.2 Ensemble modeling \n",
    "        * 6.2.1 Combining models \n",
    "    * Prediction \n",
    "        * Predict and Submit results "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  1. Introduction \n",
    "## This script follows three main parts \n",
    "* Feature analysis \n",
    "* Feature engineering \n",
    "* Modeling "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns \n",
    "%matplotlib inline \n",
    "from collections import Counter \n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier,\\\n",
    "GradientBoostingClassifier, ExtraTreesClassifier, VotingClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.linear_model import LogisticRegression \n",
    "from sklearn.neighbors import KNeighborsClassifier \n",
    "from sklearn.tree import DecisionTreeClassifier \n",
    "from sklearn.neural_network import MLPClassifier \n",
    "from sklearn.svm import SVC \n",
    "from sklearn.model_selection import GridSearchCV, cross_val_score, StratifiedKFold, learning_curve "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.Load and check data\n",
    "### 2.1 Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('titanic_train.csv')\n",
    "test = pd.read_csv('titanic_test.csv')\n",
    "IDtest = test['PassengerId']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Outlier detection "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[27, 88, 159, 180, 201, 324, 341, 792, 846, 863]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def detect_outliers(df, n, features):\n",
    "    outlier_indices = []\n",
    "    for col in features:\n",
    "        Q1 = np.percentile(df[col], 25)\n",
    "        Q3 = np.percentile(df[col], 75)\n",
    "        IQR = Q3 - Q1 \n",
    "        outlier_step = 1.5 * IQR \n",
    "        outlier_list_col = df[(df[col] < Q1 - outlier_step) | (df[col] > Q3 + outlier_step)].index\n",
    "        outlier_indices.extend(outlier_list_col)\n",
    "    outlier_indices = Counter(outlier_indices)\n",
    "    multiple_outliers = list(k for k, v in outlier_indices.items() if v > n)\n",
    "    return multiple_outliers \n",
    "\n",
    "Outliers_to_drop = detect_outliers(train, 2, [ 'SibSp', 'Parch', 'Fare'])\n",
    "Outliers_to_drop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train.drop(Outliers_to_drop, axis=0).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 joining train and test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Voyager\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=True'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass sort=False\n",
      "\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "train_len = len(train)\n",
    "dataset = pd.concat(objs=[train, test], axis=0).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 check for null and missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age             256\n",
       "Cabin          1007\n",
       "Embarked          2\n",
       "Fare              1\n",
       "Name              0\n",
       "Parch             0\n",
       "PassengerId       0\n",
       "Pclass            0\n",
       "Sex               0\n",
       "SibSp             0\n",
       "Survived        418\n",
       "Ticket            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fill empty and NaNs values with NaN\n",
    "dataset.fillna(np.nan)\n",
    "# check for Null values \n",
    "dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 881 entries, 0 to 880\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    881 non-null int64\n",
      "Survived       881 non-null int64\n",
      "Pclass         881 non-null int64\n",
      "Name           881 non-null object\n",
      "Sex            881 non-null object\n",
      "Age            711 non-null float64\n",
      "SibSp          881 non-null int64\n",
      "Parch          881 non-null int64\n",
      "Ticket         881 non-null object\n",
      "Fare           881 non-null float64\n",
      "Cabin          201 non-null object\n",
      "Embarked       879 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 82.7+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            170\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          680\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      int64\n",
       "Survived         int64\n",
       "Pclass           int64\n",
       "Name            object\n",
       "Sex             object\n",
       "Age            float64\n",
       "SibSp            int64\n",
       "Parch            int64\n",
       "Ticket          object\n",
       "Fare           float64\n",
       "Cabin           object\n",
       "Embarked        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>881.000000</td>\n",
       "      <td>881.000000</td>\n",
       "      <td>881.000000</td>\n",
       "      <td>711.000000</td>\n",
       "      <td>881.000000</td>\n",
       "      <td>881.000000</td>\n",
       "      <td>881.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.713961</td>\n",
       "      <td>0.385925</td>\n",
       "      <td>2.307605</td>\n",
       "      <td>29.731603</td>\n",
       "      <td>0.455165</td>\n",
       "      <td>0.363224</td>\n",
       "      <td>31.121566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>256.617021</td>\n",
       "      <td>0.487090</td>\n",
       "      <td>0.835055</td>\n",
       "      <td>14.547835</td>\n",
       "      <td>0.871571</td>\n",
       "      <td>0.791839</td>\n",
       "      <td>47.996249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>226.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.895800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>448.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   881.000000  881.000000  881.000000  711.000000  881.000000   \n",
       "mean    446.713961    0.385925    2.307605   29.731603    0.455165   \n",
       "std     256.617021    0.487090    0.835055   14.547835    0.871571   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     226.000000    0.000000    2.000000   20.250000    0.000000   \n",
       "50%     448.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.000000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    5.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  881.000000  881.000000  \n",
       "mean     0.363224   31.121566  \n",
       "std      0.791839   47.996249  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.895800  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   30.500000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# summarize data \n",
    "train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.Feature analysis\n",
    "### 3.1 Numerical values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# correlation matrix between numerical values(SipSp Parch Age and Fare values) and Survived \n",
    "g = sns.heatmap(train[['Survived', 'SibSp', 'Parch', 'Age', 'Fare']].corr(), annot=True, fmt='.2f', cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Only Fare feature seems to have a significative correlation with the survival probability \n",
    "\n",
    "It doesn't mean that the other feature are not useful.\n",
    "\n",
    "Subpopulations in these features can be correlated with the survival. \n",
    "\n",
    "We need to explore in detail these features "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SibSp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x23ee8205048>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.factorplot(x='SibSp', y='Survived', data=train, kind='bar', size=6, palette='muted')\n",
    "# g.despine(left=True)\n",
    "g.set_ylabels('survival probability')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Parch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x23ee8211cc0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.factorplot(x='Parch', y='Survived', data=train, kind='bar', size=6, palette='muted')\n",
    "g.set_ylabels('survival probability')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Voyager\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "C:\\Users\\Voyager\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(train, col='Survived')\n",
    "g = g.map(sns.distplot, 'Age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x28f71cc6358>"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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geocn894TB/NNIrkpBXf2v8yPdR7hwgVFv74aaW0RwnKSSETZEBcHqaklWhZFa3hsVlO+/rUmD/U/yXP3HMPVxfLRKEGd6rLC1J/jxzQDB8LFi8UORYhKRRKJKBvM6xrRtGmxLzH5w0Z8urIO9/U6zX29Em96onpiaC/aO0TzXuSXbNkCDz1U7FCEqFQkkYiyITramIgYGFis0xesqcnMJfUY3OUso/udLtY1sj2qkdyiEyP3v8CDo0wsWgRFrNcnhEASiSgrYmKMjvZi7M++53AVHnu7KaGN0hh3x4kSLZmVGNYP16QTPNrqL5o2hbFjwbxRnhCiAJJIhP2ZTMYGU8Vo1krPdGTo1Fa4uZh44f6jJV4wOKn1LWS7VqHu71/xzDPGCvMTJpTsmkJUdJJIhP0dPGjMai9GIpnySRD7jlfh+fuP4uudXeJQTC7uJAXfiv8fS2lc7zL33mtsWfLnnyW+tBAVliQSYX9Xl/i/yRFbG/dUZe6yugzukkRYk9JbJysxrB/O6Rfw3bSK4cPBzw/+/W9jVJgQ4kaSSIT9xcQYHe0NGlh8StYVxSMzmuLvfYWH+p8q1XDON21PlpcvNdYuxM0N7r/fWN1+lWzwLES+JJEI+4uONqaS30QHx4zF9dhzxJMJdyVQxc1UquFoRycSQ3vht2EFjukp9O8PderA888b3TlCiOtJIhH2ZTIZK/7eRP/I6WQXXvu6AV2DL9CpdapVwkoM64fDlcv4r/8eJyd44AHYscNYIFgIcT1JJMK+Dh0y9gFp1MjiU16Z34DLVxSPDjxptbBSGwST6VMX/98XA9CjB9SvDzNmSF+JEHlJIhH2tXOn8WzhKon7j7nzyY91GNDxHHX9s6wXl1KcDe2FT/QvOKck4eAAd91ltMJd3Q1YCGGwaiJRSvVVSu1XSsUppZ7L53NXpdRi8+eblFKB5uO9lFIxSqld5ufbcp2zznzN7eZHDWt+B2FlO3YYS8db2NH+/LyGuDqbGNn7jJUDg8SwvihTDn5//heA3r2halWYNcvqtxaiXLFaIlFKOQJzgX5AS2CEUqplnmIPAee11o2BWcAb5uNJwECtdTAwCliQ57x7tdah5keitb6DsIGdOyEgACzYnS1mvyff/enP3d0Tqe5V8jkjRUmv05SLNRpQw9y85eYGAwbAsmVw+LDVby9EuWHNGkkkEKe1PqS1zgIWAYPylBkEzDe/Xgr0UEoprfU2rfXVBvA9gJtSytWKsQp72bEDLNzc6PVv6uPpns2Q7jZa410pEkN7U23HOlySjfW77rjDqEC9955tQhCiPLBmIqkLHM/1PsF8LN8yWutsIAXwzVPmLmCb1vpyrmOfm5u1XlSy2XP5lZZm/GlvQUf7gePufPenP4M6J+FRysN9C5PYtg/KZML/j6UA+PtDt27w2WeyzLwQV1kzkeT3Cz7veJdCyyilWmE0dz2a6/N7zU1eXc2P+/O9uVJjlFLRSqlo2RO6jNq923hu2LDIom8uqo+Lk+bObrZdQfFirUak125Mjd8WXTs2YACkpMD339s0FCHKLGsmkgSgXq73AUDe8ZrXyiilnABvINn8PgD4HhiptY6/eoLW+oT5OQ1YiNGEdgOt9cda63Ctdbi/v3+pfCFRyq6O2CqiRnLirAtfrqlJ38hz+NigbySvxLZ98N79N66JRgU7JMSYoPj55zYPRYgyyZqJZAvQRCkVpJRyAYYDy/OUWY7RmQ4wBPhNa62VUtWAlcAUrfW1wZZKKSellJ/5tTMwANhtxe8grGnnTmN/9po1Cy327ncBmEyKYbfap2Z5NrQ3AP7rjNmIDg7Qpw+sXQtHjtglJCHKFKslEnOfx3hgNbAXWKK13qOUmqaUijIXmwf4KqXigKeAq0OExwONgRfzDPN1BVYrpXYC24ETwCfW+g7CynbuNDraC+nmyrzswLxVtekSnEJtXyvOGylEpn990uq1vK55q08fI+wvvrBLSEKUKTe/i9BN0FqvAlblOfZSrteXgKH5nPcq8GoBl21XmjEKO9HaSCTduxdabMnv/iSnOTOos313l0oM7U2jFe/gdvIQl+o0pGZNCA83mrdeesmopQhRWcn//sI+jh2D1NQi+0fm/lCXBjUvEdq49JaJL47E0F4A1+aUAPTta3yN33+3V1RClA2SSIR97NplPBcyhyRmvydb9lUlqlNSibbPLQ2XfeqQEhhyXSLp3BmqVIFvvrFjYEKUAZJIhH3ExhrPgYEFFvlgeV3cXHLoHZFsm5iKkBjaG8/4HVQ5tg8wtlDp3Bm++w6y7NN9I0SZIIlE2EdsrLH1oJdXvh+nZjiycG0NeoSdx9O9bGwCcja0J1qpaysCA9x6K1y4AGvW2DEwIexMEomwjz17Cl2ocekf/mRedqR/+7JRGwHI8q5BSsMwY/SWeS358HBjIcdFi4o4WYgKTBKJsD2TCfbuLTSRfLmmFgH+l2jRoGytQ5LYtg8ex/bhccjo43F2hq5djYUcZckUUVlJIhG2d/w4ZGQUmEiOnHbjjx3V6B1+3u6d7HmdDemBdnC8rtP9ttuMr7NypR0DE8KOJJEI2yuio/2rX4yZ7r3Cz9soIMtd8fThfOMII5GYm7dCQsDXFxYvLuJkISooSSTC9q4mknxqJFrD/NU1CW2URi2fsjkUKjGsL+4n46m6dxMAjo7G6K2ff4bMTDsHJ4QdSCIRthcbCz4+4O19w0eb9lYl7kQVekWUvdrIVWdDepDj7ErNNf/bb61zZ6N5a+1aOwYmhJ1IIhG2V8iIrUW/1cDFyUS3NhdsHJTlctw8SWrdnRq/LUJdMWpNbdsa608uW2bn4ISwA0kkwra0LnDElskES//wI7x5WpmZO1KQM+G345yWjM+mnwBj9Fb79vDDD5CTY+fghLAxSSTCtk6cMNbYyieRbNpblRNJbtxShmsjV51v1pEsL19q/fK/5q0uXSApCTZutGNgQtiBJBJhW4WM2Fr6hz/OTiY6tU6xbUzFoB2dSGzbB98NK3BKM/pzIiONmok0b4nKRhKJsK0CRmxpbSSS8KZlv1nrqtPht+OQnXVtwysPDwgLM7bg1Xk3lRaiApNEImwrNhaqVYPq1a87vGWfF8cS3egWUvabta5KD2hBRq2G1Pzl+tFbhw4Z4wmEqCwkkQjb2rMH6te/4fC3f/jj5GiiS3CqHYIqJqU4064/1Xatx+3kIcBIJEoZtRIhKgtJJMJ2tDZqJHn6R7SG7//yJ6xJOp7u5WvI05l2/QGo+ctXgDE9pmVLSSSicpFEImzn9GljzfU8/SP7jlUh/qQ7nctBJ3tel6vX5nyTCKN5y9wx0rkzbNtm7J4oRGUgiUTYTgEd7Ss2+ALQsVU5atbK5Uy726lyIu7akilduhjHf/jBjkEJYUOSSITtXE0kebbXXbHBlyYBF/GvdsUOQZWcsWSKG7V++hyAevWMXCnDgEVlIYlE2E5srLELVK4RW0kpzmyI9aZjy/JZGwFjyZSzob2osfYbHDPTAaN5648/ILns7MslhNVYlEiUUq2tHYioBGJjjRFbuTYZWfWPDyaTolOr8tc/ktupDoNxykzD//clgLHZVU4O/PijnQMTwgYsrZF8qJTarJQaq5SqZunFlVJ9lVL7lVJxSqnn8vncVSm12Pz5JqVUoPl4L6VUjFJql/n5tlzntDMfj1NKzVaqrG19JPKlNezefWP/yEZf/LyzaBJQvtdfTwkKJaNmQ2r/+AkATZsaW9KvWGHnwISwAYsSida6C3AvUA+IVkotVEr1KuwcpZQjMBfoB7QERiilWuYp9hBwXmvdGJgFvGE+ngQM1FoHA6OABbnO+QAYAzQxP/pa8h2EnZ09a7Tz5Br6m3VFsXqzD+1bpOJQ3htZleJUhzvw3vsPHod34+AAHToYe5Rcvmzv4ISwLot/fLXWB4EXgGeBW4DZSql9Sqk7CzglEojTWh/SWmcBi4BBecoMAuabXy8FeiillNZ6m9b6pPn4HsDNXHupDVTVWm/UWmvgS+AOS7+DsKN8Rmyt3+VNWqZTuR2tldeZ8NsxOTpfq5V06gTp6UZfiRAVmaV9JG2UUrOAvcBtGLWFFubXswo4rS5wPNf7BPOxfMtorbOBFMA3T5m7gG1a68vm8glFXFOURfks1vjzZh+cHE2ENUm3T0yl7IqnD2fb3Eat1fNxyMwgLAxcXWH5cntHJoR1WVojmQNsBUK01uO01lsBzLWGFwo4J7++i7xL2RVaRinVCqO569GbuObVc8copaKVUtFnz54tIERhM7GxxqqGfn7XDv202YfgoAzcXcvHIo2WONllGE4ZKdRcuxBXVwgPNxKJLOIoKjJLE0l/YKHWOhNAKeWglKoCoLVeUMA5CRh9KlcFACcLKqOUcgK8gWTz+wDge2Ck1jo+V/mAIq6JOa6PtdbhWutwf39/i76ksKKruyKax0acTHJh92FPIpqn2Tmw0pUSFEp6nabUXTYXtKZjRzh+HHbutHdkQliPpYnkV8A91/sq5mOF2QI0UUoFKaVcgOFA3kr+cozOdIAhwG9aa20eGbYSmKK1/vtqYa31KSBNKdXBPFprJCDzh8uD2Njr+kdWb/EBILJFxegfuUYpTnQZhmf8Drx3/03HjkbulOYtUZFZmkjctNbXGrLNr6sUdoK5z2M8sBqjb2WJ1nqPUmqaUirKXGwe4KuUigOeAq4OER4PNAZeVEptNz9qmD97HPgUiAPigZ8s/A7CXpKSIDHxhv4RP+8sGta+ZL+4rORMWD+y3b2os2zutUUcZRiwqMicLCyXoZQKu9o3opRqBxQ58F9rvQpYlefYS7leXwKG5nPeq8CrBVwzGpAJkuVJno72nBz4Jbo67VumUhFnAZlc3TkVGUXdPxZz6LG36NAhgHnz4ORJqFPH3tEJUfosrZFMBL5VSv2llPoLWIxRaxCiaHmG/m7ZX5Xz6c5ENKtY/SO5neg6AmUyUfe/79G5s3FMZrmLisrSCYlbgOYYzUpjgRZa6xhrBiYqkNhYqFIFahitkz9v9kEpTbsKnEgu+dblbJse1FnxEY1qpFG7tjRviYrrZuYTRwBtgLYYs9RHWickUeHkGbH1a0x1mgVcxNujfG1idbOO33o/Thkp1P7pMzp2hF9/hYsX7R2VEKXP0gmJC4AZQBeMhBIBhFsxLlGR5BqxlZ7pyKa9XrStIJMQC5PWIJiUoFAClr5Dp/Y5XLpkJBMhKhpLO9vDgZbmZUmEsFxysrEzormj/c8d3mTnOBDWtOI2a+V2/NaRtP7sKXpcWIqHxzCWL4eoqKLPE6I8sbRpazdQy5qBiAoqT0f72q3VcXYy0Toow45B2U5Sq1vIqNWIxt+8SmSE5scfwVRxJvILAVieSPyAWKXUaqXU8qsPawYmKog8Q39/3VqdVoEZuLlUksqtgwNHe47G88huevvFcOYMbN5s76CEKF2WNm1NtWYQogLbswfc3aFGDc5ecGZnvCcP9T9l76hs6mxobzJ//pAR257hRae1LFum6NDB3lEJUXosHf77B3AEcDa/3oKxiKMQhbva0e7gwO/bjD3RwppUjv6Rq7SjE0d7PEiD+N8JDzrHd9/JIo6iYrF01NYjGPuFfGQ+VBdYZq2gRAWSa8TW2q3V8XDLoVm9yjcG9kz4AC751GFoyqfExf2vxU+IisDSPpJxQGcgFa5tclWj0DOEuHDBWBfEnEh+jalOSKN0HB3tHJcdaCdnjvQew/DE2QB8/72dAxKiFFmaSC6bdzkEri35LpVzUbhcHe1HT7ty6JQ7bStZs1ZuZ8Jvp5q/CxGuO/j+v/LjIyoOSxPJH0qpfwPu5r3avwVkwQdRuD17jOfAQNZurQ5Au6YVfyJiQbSjE0f6PsqQy1+zdZvi6FF7RyRE6bA0kTwHnAV2YexWuIqCd0YUwhAbC25uULMma7dWx8frCoG1Kt6y8TcjMbQPvWtsB2DZ0mw7RyNE6bB01JZJa/2J1nqo1nqI+bXUzUXhYmOhfn20cmDt1mqENkmvkMvG3xQHB9Sdd9KaXSydc9re0QhRKiwdtXVYKXUo78PawYlyzrxYY+yRKpw571rphv0W5HyzDgzw38z6IwEkbE+ydzhClJilTVvh/G+xxq7AbOArawX9bHxVAAAgAElEQVQlKoCUFDhxQvpHChA2uD4AS8f+ZudIhCg5S5u2zuV6nNBavwPcZuXYRHm2d6/x3KABa7dWp47vZWr5ZBV+TiXi38KfVp5HWLyxHuzaZe9whCgRS5u2wnI9wpVSjwFeVo5NlGfmEVvZ9YJYt71apR72W5AuHU38Q0eOPv66THUX5ZqlTVszcz3+D2gH3G2toEQFEBsLrq7EpDYh9aITYdKsdYNukcYItiV/14GVK+0cjRDFZ9GijVrrW60diKhg9uyB+vVZu90XqHzra1mijl8Wzeuls/jM/UyeNBR69wYXF3uHJcRNsyiRKKWeKuxzrfXbpROOqDD27IHmzVm7tTqN6mRSzbNib6tbXLe2TeGD5W3YfwCavfUWPP+8vUMS4qbdzKitxzEWa6wLPAa0xOgnkb4Scb3UVEhIILNuY/7e7S39I4XoEXYeB6X5sv4L8OqrEBdn75CEuGk3s7FVmNZ6ktZ6EkYfSYDW+hWt9SsFnaSU6quU2q+UilNKPZfP565KqcXmzzcppQLNx32VUr8rpdKVUnPynLPOfM3t5ocsHlnWmNfY+lt14fIVB9pVkm11i8PXO5uI5qnMT7+LHAdnGDtWOt5FuWNpIqkP5B67mQUEFnaCUsoRmAv0w6i9jFBKtcxT7CHgvNa6MTALeMN8/BLwIvB0AZe/V2sdan4kWvgdhK3s2AHA2uS2ODmaCGlUObbVLa4+Eec5kVyF33v/H/zyCyxaZO+QhLgpliaSBcBmpdRUpdTLwCbgyyLOiQTitNaHzCsHLwIG5SkzCJhvfr0U6KGUUlrrDK31eoyEIsqbnTvBw4Nf9wfQov5F3F1lk/LCdG6dgpd7NvPT74TmzWHiRDh/3t5hCWExSyckTgceBM4DF4AHtdavFXFaXeB4rvcJ5mP5ltFaZwMpgK8FIX1ubtZ6UalKv3pT2bNjBxfqt2HrQS/aNpFhv0VxcdZ0D73Ad3/VIPXxZyEpCaZMsXdYQljM0hoJQBUgVWv9LpCglAoqonx+v+DzNv5aUiave7XWwRhLtXQF7s/35kqNUUpFK6Wiz549W8QlRanRGnbuZJ3XQEwmRZj0j1ikb2QymZcdWXK8I9x1F3z0EWzYYO+whLCIpTPbXwaeBa7+meRM0WttJQD1cr0PAE4WVMa8WZY3kFzYRbXWJ8zPacBCjCa0/Mp9rLUO11qH+/v7FxGqKDVHj0JaGmuzuuDmkkPLBpVvW93iaNHgIg1rZ/L+sjroBx6EmjXhkUfg8mV7hyZEkSytkQwGooAMAK31SYoe9rsFaKKUClJKuQDDgeV5yiwHRplfDwF+K2x5eqWUk1LKz/zaGRgA7LbwOwhbMHe0/3qqNcENM3B2khFIllAKBnVJYlucF/8crmn0k8TGwuuv2zs0IYpkaSLJMv+C1wBKKY+iTjD3eYwHVgN7gSVa6z1KqWlKqShzsXmAr1IqDngKYwMtzPc4ArwNPKCUSjCP+HIFViuldgLbgRPAJxZ+B2ELO3dyjPrsO1OdiGbSrHUzerU7j6d7NnO+rwsdOkCPHjB9+v92mhSijLJoZjuwRCn1EVBNKfUIMBoLfoFrrVdh7KaY+9hLuV5fAoYWcG5gAZdtZ2HMwh527OCX6kPhPIRLIrkp7q4m+kYk8+0f/swcG0+t8eMhOhoefhjWrwdHR3uHKES+LB21NQNjeO53QDPgJa31e9YMTJRTO3aw2rE//tWyKv22usUxqEsSV7Id+OTH2lCtGowbB//8A++/b+/QhChQkYlEKeWolPpVa/2L1nqy1vpprfUvtghOlDMZGeTEHebX1EjaNU2TbXWLIcA/i8jmqbz/Qx0uZTlAz54QEWEMBz52zN7hCZGvIhOJ1joHuKiU8rZBPKI8272bGMI4n+Up/SMlMKJHIqeTXZm3spbRC//UU2AyweOPy/IpokyytLP9ErBLKTVPKTX76sOagYlyaPt2VtMHpbSsr1UCIY3SCW6Yzuvf1CfrioJatWD0aFi1Cr75xt7hCXEDSxPJSoy1r/4EYnI9hPifmBjWOPajWcBFvGXZ+GJTCu7rdYaEs27MX13LODh4MLRsCRMmGDPfhShDCk0kSqn6AFrr+fk9bBOiKC9SN+1lY04k7aRZq8QimqXRvH4Gr31dnyvZyhix9fTTcOEC/Otf9g5PiOsUVSNZdvWFUuo7K8ciyrNLl1i9J4AcnIhsLomkpJSCkb3PcOS0uzGCCyAoCO65B776Cn7+2b4BCpFLUYkk97ibhtYMRJRzu3axIqcf1VwzaRUoy8aXhg4tU2nbOI0XPwsiOdU85evee6F+fRgzBtJlQUxRNhSVSHQBr4W4TvamGFbRnw5Nz8m8uVKiFIwffIIL6U68/EWgcdDFxWjiSkiAF16wa3xCXFVUIglRSqUqpdKANubXqUqpNKVUqi0CFOXDxp8ucA4/2re9Yu9QKpSGdS4R1TmJD36oy+7D5pWJgoNh0CCYPRs2bbJvgEJQRCLRWjtqratqrb201k7m11ffV7VVkKLsW7G5Js7qChEtpH+ktD3Y9zQe7jmMmdGU7Bxza/PDD4OfHzz2GGRn2zdAUendzH4kQuQvM5MVSR1oX/0gHm6yG2Jpq+qRw4Q7E9gY683rC+sbBz08jOVTtm+X5VOE3UkiESV28Mf97KMFXZqesXcoFdZtYRfoEZbM1C8C2bzXvINDt24QGWn0lZw6Zd8ARaUmiUSU2IpvjNFDEZHyv5M1TRxyAj/vK9z7agsupDsZvfFPPmlsfjVpkr3DE5WY/OSLElv0Zx1CHHbiEyjdZtbk6Z7D8/cd5cgZN4a83MqYqFi3rjG35JtvYO1ae4coKilJJKJEDh6ELecackeNDchyv9YX3DCDSXcnsHZrdca+08RYw3HECCOhjB0rW/MKu5BEIkrkm0/SUJjoGSz9I7bSNzKZe3ue5tOVdZj6RSDa2cVo4jpwAGbOtHd4ohKydIdEIW6gNXz9FdzCH1RpGYRMLLKd0f1Ok5zmzLQvAzFpmPYgqFtugf/8x6ihBAXZO0RRiUiNRBTb1q1w4JQXIxyXkBbQwt7hVCoODvD03ce5vcM5Xl0QyLMfNUSPHWc0L06YYO/wRCUjNRJRbF9/DS4qi9719nLEydne4VQ6Dg7w1NDjODmaeGtxfU4nu/Dp/Q/h8vEcWL4coqLsHaKoJKRGIoolKwu++dpEP/0TqnFje4dTaTk4wIS7TjC63ykW/FKL/lumkhrYBp54AjJk8UxhG5JIKpgTJ+DZZ2HoUOjUyVgkdseO0r/PkiVwOtGBR/mQlKCQ0r+BsJhScH/vMzw74hh/7KxO16xfOXnsCkyfbu/QRCUhiaQC+fFHCAmBt9821vLLyIAvv4TQUOjZ00gypUFrY3BQk2pn6cNqUgMlkZQFfSOTmf7wYeLO+dDRbRt73/oR9u2zd1iiErBqIlFK9VVK7VdKxSmlnsvnc1el1GLz55uUUoHm475Kqd+VUulKqTl5zmmnlNplPme2UjJ5AYw/PgcOBB8fmDfPSCDvvgvffguPPw4bN0J4uPFcUuvWGUs8jav2NRdrNyK7ikxELCsim6cxa1wc6U7V6Jz9B+vv+9DI/EJYkdUSiVLKEZgL9ANaAiOUUi3zFHsIOK+1bgzMAt4wH7+EsUf80/lc+gNgDNDE/Ohb+tGXL6tXG8st9egBc+YY+x5d5eUFd99tHHd0hFtuMZJLScycCdWrax4+9R9SGoaV7GKi1DWtl8mcifFU87xCz5jX+W7SBnuHJCo4a9ZIIoE4rfUhrXUWsAgYlKfMIODq3u9LgR5KKaW1ztBar8dIKNcopWoDVbXWG7XWGvgSuMOK36HMO30aRo6Ehg1h8mRj36P8BAXBBx9A8+bGNIPiJpPYWFi5Eoa1P4LH5WSSm3UofvDCamr7ZjHrmZOEuuxl6KyOzH7zUtEnCVFM1kwkdYHjud4nmI/lW0ZrnQ2kAL5FXDOhiGtWGlrDqFGQkmLUSFxdCy/v5QVvvAEtWxYvmWhtrFzu6QmPVPkak6MTF5pEFP8LCKvy9oKZY/ZxB8uY8KwbkyeDSVb5F1ZgzUSSX99F3sZaS8oUq7xSaoxSKlopFX327NlCLll+ff89rFlj7G1k6URmd/fiJ5PPPjP6Rx59FJrs/C+pgW3IcfMsVuzCNq40asG7HRYxjjnMmGH8ISBdJqK0WTORJAD1cr0PAE4WVEYp5QR4A8lFXDOgiGsCoLX+WGsdrrUO9/f3v8nQy76cHHjpJaM/ZODAmzvX3R1ef/3mksmpU8ZW4aGhMKhjIl5x20hu1ql4wQubOjpgLG9XeZEJ/l/x4Yfw4ov2jkhUNNZMJFuAJkqpIKWUCzAcWJ6nzHJglPn1EOA3c99HvrTWp4A0pVQH82itkcAPpR962bdkCezZYzRtOTre/PlVqlyfTGbNKvgv1bQ0uO8+uHjR2PbCd+svAJyX/pFyIdujGocGPsmss/czNOQA06cb/72FKC1WSyTmPo/xwGpgL7BEa71HKTVNKXV17YZ5gK9SKg54Crg2RFgpdQR4G3hAKZWQa8TX48CnQBwQD/xkre9QVmVnw8svGx3s3bsX/zpXk0mnTvDUU8borvPnry+TkABdu8IffxhlAgLAZ8tqsjyry/pa5cjpyEGkBoYw/3A3bumUxaRJsGKFvaMSFYUqpAJQYYSHh+vo6Gh7h1FqFiwwRmpNm2b8ki8prY3mrY8/BicnGDDA2MF1+3b45RfIzDSa0SIjjcKd7qrN+aC27L3//0p+c2Eznif2027mPcT3H8fA+NkkJMCGDRAcbO/IRFmllIrRWocXVU5mtpdDs2dDYCB06VI611PKqI189BHcfjv89puxzMratdCihTGxMTLSKOu1dzMu58+Q3Lxz6dxc2Ex63WYkdB1B45Xv8c6ITbi5Gf1rSUn2jkyUd7L6bzmzZQtERxv7GJX2nP5GjYy1/saOhfR08Pa+sUyN3xdjcnImqfUtpXtzYROH+4/Db886un58H6++sIsnJrsxcqSxvI6D/Fkpikn+1yln3n/fGHXVu7f17uHomH8SwWTCf923JDfvRI67l/UCEFZjcnVn/7CXqHIijv4bXmD8ePjpJ6OvTIjikkRSjiQnw6JF0KsXeHjY/v5V92zELSmBxFArZjFhdReaRHKy410ELJ3FvY3+4bbbjCHBv/9u78hEeSWJpBz54gu4dMl++xXVWLcEk5ML51pJs1Z5Fx81kcveNWj+5miefvIyAQHGEO9z5+wdmSiPJJGUE1rDhx9C69ZGX4bN5eTgv+5bzrXoQo6bHapDolTluHlyYOgLeBzbS8ul/+GFFyAxER5+WGa+i5sniaSciI6GgwehXz/73L/azj9xTT5FYltp1qooklt05lRkFPUXvk6YKZpHHoFly+CTT+wdmShvJJGUEwsXgrMzdOtmn/vX+eF9rlSpyrlWdgpAWEX8oElc9vanxX/uYdjt6YSHw8SJsh+WuDmSSMqBnBz45hvo0MFYedfWXBOP4//X95xqPxiTi7vtAxBWk12lKnvvfRX3k/E0nfMEzz5rbEVwzz1w+bK9oxPlhSSScuD33+HMGWPjKnuos+Ij0CZOdh5qnwCEVaU0asfRXg9R++cvaLl9IZMnw7ZtxtYEQlhCEkk5sHChMdy3gx3WSHTIukTtFR9zrlU3LvlW2q1fKryjvcdwoWFbms14hF519hAVBTNmwK+/2jsyUR5IIinjLl2CpUuNNbWK2rjKGvx/W4xLyllOdBlu+5sLm9GOTsSOfINs1yq0fulOxt+fQmCgsaabLKEiiiKJpIxbtcpYxt0ezVrqShaBX04jvU5Tzjdtb/sAhE1lefsTO/IN3E/G0/bt+3l+Sg5JSfDQQzIkWBROEkkZt3Ah+PhA27a2v3ed5R/ifuoQhwZYYWEvUSalNArj4ODJ+G1cQZ9fnuaRR2D5cmNBTyEKIomkDEtJMRbT6969eJtXlYRjegoNFvyH800iSW4uOyFWJie7DCOh6wjqLX2HJxznEhFh7EWzd6+9IxNllSSSMuy//zWGYNqjWav+N2/gkpJE/MCJUhuphOLumERSy240m/MEb3b+AVdXYydNGRIs8iOJpAz7+muoW9fYE8SWvPZupv6iNzkdMZD0erILYqXk4EjsqNdJCQql23tDmBa1hR074N//tndgoiySRFJGnTplzB+57TbbVggcM9Np8eq9XPauQdzgyba7sShzTC7u7Hr4XTLqNGXcN10Z1uEob78Na9bYOzJR1kgiKaOWLAGTyfbNWo3nTMT9VDx775lGtuw5UunluHuxc8wcMmo2ZN6WYBrXSGHUKDh71t6RibJEEkkZ9fXX0KQJNGhgu3vW++ZNaq+ax7Eeo0lpXOQ2zaKSuOJZne1jPyK7fiOWnr2F82ezGT1ahgSL/5FEUgbFxRlb6tqyNlJr1Wc0+vhZzoT15XC/sba7sSgXcty92PHo+wQ09+KNnEn8+CPMfc9k77BEGSGJpAxauNDoF7ntNtvcr/bKT2k28xGSm3Vk34hpsnm3yJfJ1Z3dD83izs5nGMAK/jXRxPqfUu0dligD5DdGGaO10awVEgL+/la+mclE0KfP02zGI5xv2oE9D85AOzlb+aaiPNOOTsQPeY6Xo7YRqA8zZMBlTvy0095hCTuzaiJRSvVVSu1XSsUppZ7L53NXpdRi8+eblFKBuT6bYj6+XynVJ9fxI0qpXUqp7UqpaGvGbw/btsGBA9Zv1nJOSaL184No8PVrnOxwJ7sefocc1yrWvamoMNJvjWL6/XtJN1XhrtszyZz6BmRn2zssYSdWSyRKKUdgLtAPaAmMUEq1zFPsIeC81roxMAt4w3xuS2A40AroC7xvvt5Vt2qtQ7XWFa5HeOFCcHKy7gZW1WN+JXx0G3yiV3Nw8DMcuPsFtKPURMTNqRFWnykjDrNZRzDylYaYOnWRHbEqKWvWSCKBOK31Ia11FrAIGJSnzCBgvvn1UqCHUkqZjy/SWl/WWh8G4szXq9BycmDRIoiMhKpVS//6jhmpNJ35KCFP9yLb2Y2tExdwotsImbkuiq1zZDaPRZ1iKUN5Zse9xqJw77xjjF0XlYY1E0ld4Hiu9wnmY/mW0VpnAymAbxHnamCNUipGKTXGCnHbzbp1cOKEdZq1qsWsJWJ0MLVXfsqxW0cS89RC0us2K/0biUpnaPezDO5ylplZTzCn1qvwr39B586wU/pOKgtrJpL8/szNO/K8oDKFndtZax2G0WQ2TimVbyOQUmqMUipaKRV9tpzMnvryS2Mr3c6dS++ajpnpNHlnHKFP98SEA9ue/JxDUf/C5OJWejcRlZpSMG7wCTq3vsCEY0+xfMh8o4krLAyefRYyMuwdorAyayaSBKBervcBwMmCyiilnABvILmwc7XWV58Tge8poMlLa/2x1jpcax3ub/XhTyWXng7ffQe33FJ6G1h57/yL8IdCqLP8A453v4/oSd+QGtimdC4uRC6ODvD8fcdoGnCR4SvuZfO/l0GfPvDmm9CqlbGxjqiwrJlItgBNlFJBSikXjM7z5XnKLAdGmV8PAX7TWmvz8eHmUV1BQBNgs1LKQynlBaCU8gB6A7ut+B1s5r//Nf5w69On6LJFyskh8POXCZ14C2RlsX3cp8QPmiS1EGFV7q4mpj98mOqe2dw+vSOxQ182+ksAbr8d7r4bTub9W1JUBFZLJOY+j/HAamAvsERrvUcpNU0pFWUuNg/wVUrFAU8Bz5nP3QMsAWKBn4FxWuscoCawXim1A9gMrNRa/2yt72BLX34JdepA69Ylu45TyjnaPNefwC+ncTpiINGTF5PSKKx0ghSiCD5e2bzxaDxaK3pOCiHerz18/DGMHg0//GAsZT13rjGyRFQYSleCBXPCw8N1dHTZnXJy/LixptaoUcajuNwTDtLm2X64Jh7j4J3PcarDYBmRJezi8Ck3Js5tTHXPbP6avY16NS4bI0lmzYKYGIiIMBJMaKi9QxWFUErFWDLNQma2lwFffmnMaO/Vq/jXqLp7A2HjOuKYmsz2cZ9wquOdkkSE3QTVvsSbj8aTlOJEz0khnEl2NjbXeestY1OTgwchPBwmT5bO+ApAEomd5eQYf5iFhRlNW8VRPeZXQp7uyRVXT7Y9+QWpgSGlG6QQxdCsXib/98hhjp5xpffkEJJTnYw/bnr1gvnzoW9fmDHDaO768Ud7hytKQBKJnf30Exw7BlFRRZfNj88/qwieMoBM33pse+IzMv3rl26AQpRAcMMMXn3oMHuPVaHP5DZcSHcyPqhaFZ5+GmbPBkdHGDgQhgwxmr9EuSOJxM4++AD8/Io3d6T65tW0fvEOMmo1YvvYj7ji5VP6AQpRQuHN0nnlgSNsj/ek99O5kglAcLBRJX/4YaNW0qIFvPeedMaXM5JI7OjwYaNG0q+fsb7WzfDetZ7WLw0mo2ZDdjz2Adke1awTpBCloGOrVF554Ajb4jzpM7kNKem5ls5zdoZ774V586B5c3jySejQwVjBVJQLkkjs6OOPjSbjAQNu7jzPuO0EP3c7l71rsvPR98muYoWFuYQoZZ1apzL1gSNsPehJn2dCrk8mYHTGv/EGvPACxMcbnfFPPAHnz9snYGExSSR2kpEBn34KHTtCjRqWn+d6+ijBz/Yn27UKOx7/UJqzRLnSuXUqL486QswBT/o+04bUjDzJRCljsbn5842Ow/ffh6ZNjdqKLARZZkkisZNPP4WkJBg+3PJznNLO0+bZfjhmprPzkTlcrlbTegEKYSVdglN5edRRog945Z9MALy8YMIE+OgjqF3b6EPp2BH++cf2AYsiSSKxg6wsYzh9SIjlM9lV1mVav3AH7ifj2D36bS7WbmTdIIWwoi7BKbw08ihb9nvR79k2pF3MJ5kANG4M775rzD2JjzeSyZAhxu5vosyQRGIHCxYYoxzvucfCE0wmWrw+imo7/2TfiGlcaFzh9vMSlVDXNim8cP9RNu2tWngyuTr3ZMECeOABY4RKy5bw+ONw+rRNYxb5k0RiYzk58PrrRrNvRIRl5zT66Blq/L6Y+Kh/kRjW17oBCmFDt4Sk8OL9R/gntir9nw0mPbOAZALg7m6sIbRggTHv5NNPoVEjY6n6pCTbBS1uIInExhYsgLg4ozZiyQomAUtmUm/JTBK6Dud49/utH6AQNnZLaAov3H+EjbHe9H82uOCayVU+Pkb/yRdfQKdORjtxUBA8/zwkJ9skZnE9SSQ2lJYGU6YYtfKuXYsuX/OXr2j8wdMkhvQi7o6nZe0sUWF1D03h+fuOsmGPN7c9FUJSinPRJ9WtaySPzz839qf+v/+DwEB46SUZMmxjkkhs6LXXjCbd8ePBoYh/+eqbV9PsjQc53ySSvfe9Cg5F/JUmRDl3a9sL/Gf0YXYd8qTrk6EcT7Rwh7cGDeDFF40hwmFh8J//GDWUV16BlBTrBi0ASSQ2Ex8Pb79tbFzVokXhZb32baH1y3eRUasxu0fPRDu52CZIIeysY6tU3nw0nuOJrnQe35b9x9wtPzkoCKZONfpO2rQxXgcGwvTpRnOAsBpJJDZgMsHYscYyKI88UnjZKkdiCX7udrI8qrFrzHvkuHnaJkghyog2jTJ4Z3w86Zcc6fJkW2L23+TPQKNGMG2aMQelZUtjpnyDBsYol/R06wRdyUkisYGZM2HNGnj0UfD1Lbicx+HdhE7sjtaw89G5ZFX1s12QQpQhjetmMvuJgzg5am6Z2JYf1hfyg1OQpk2N2sgHHxivp0wxaihvvSV7oJQySSRWtnmzMZeqWzdjxGJBPA9uI+Rft6JRbB/3CZn+DWwXpBBlUIB/FnOePEj9mpe448Vgpn9Vn2Jt6Nq8uVEbmTsXGjaEZ54xnmfNgszMUo+7MpJEYkWJicYSKL6+xtYLBQ26qr55NaETumFycGL7uI/JrCFJRAgAX+9sZo2No2e7ZF6Y15BBz7e2bERXflq2hDffNPZACQiAp54yEsrs2XDpUukGXslIIrGS5GRj7bmTJ43RiF5e+RTSmjo/fECbKbeT6VOXrRPmS01EiDxcXTT/vvcY4wcn8PMWH0IfDue3rSXYNiE42GhvfucdqFXLmJPSuLGxQOTly6UXeCUiicQKLlwwRmcdOGA00bZseWMZx4xUWk4bTtN3xpLcrCPbx88jy/smlgEWohJRCu7qlsTcCQdxcND0mBTKg280K37tBIzF7mbNMoZT+vjAuHFGR/2MGTJs+CYpXaxGx/IlPDxcR0dH2+ReO3YYa8odOWIMHOnY8cYyvhtW0GT2E7ieTeBwv7Ecu+2BoieWCCEAuJSlWLCmFkvW+ePtkcOUe48ydtBJ3F1LsMy81rB1K3z1FWzfDp6exorDEyYYHfSVlFIqRmtd5OJ+kkhKSXa2Mdrw6aeN/wdfesmoQefmGbedwM9ewm/jCjJqNWL/3S+QGhRq1biEqKgOnXTjgx/qEH2gKrV9LjNhSAKj+53Gv9qVkl34wAH49ltYt84Yu3/77fDgg8azS+Wa01UmEolSqi/wLuAIfKq1fj3P567Al0A74BwwTGt9xPzZFOAhIAd4Umu92pJr5seaiSQnB5YtM4aq79tnbOr2739D9erG5yonm+pb1lDnhw/w++dHst08OdpzNAnd70M7lqBaLoQAYEe8B/NX12LbQS+cnUwM6pzEoM7n6Bd5Dl/v7OJf+OxZ44d79Wo4d874oY6KgsGD4dZboWrF35nU7olEKeUIHAB6AQnAFmCE1jo2V5mxQBut9WNKqeHAYK31MKVUS+AbIBKoA/wKNDWfVug181PaieTSJYiONv4fW7gQTp0y5js9/DB07gwuFxKptvNPqm9Zg9+G5bicP0OWZ3VOdB3Bia7DyXbPr+ddCFESR8+4smKDH79vq0ZymjMODpoW9S8S2TyV1kEZBNW+RL0al6nmmY23RzYOSpOdo649ruQ4kHbRkZQMJ1LSjefUi9p/ej0AAAfySURBVE6kpDmQEneWlEPnSDmTSWp2FVKoRop7LVIcq5OS7YFJOeLs4oCzi8LZ2ai4eHsbIzZ9fP73qFHDeNSsaTxq1AA/P3AsoysglYVE0hGYqrXuY34/BUBr/X+5yqw2l9molHICTgP+wHO5y14tZz6t0Gvmp7iJZPt2Y9RVYiIkHLrMwX0m9h9QbIt1IeuKA86OJroFHWdwwCYGuq/F8+wRPA7vxvXcSQCy3Tw537Q9Z8Jv51yLLmgnqYEIYW0mExxIcGfT3qrsPVqFA8ercD69ZD97Ls4mPFxz8HDLoapKpXp2Ej6XT+KbeQJvfR4nsslycOeihx+X3KqR6eJNqkN1kk3eXMiqQsolV1IuOpOdc2NfqFIaP1+oWVNTwx9q1tDUqKmoUcuBqlWNpnIPD+P56sPDA5ydjQTk4GA8cr82mYwN9LKyjC6e4q73amkicSre5S1SFzie630C0L6gMlrrbKVUCuBrPv5PnnPrml8Xdc1SM2KE0VxlcKUuCTThIE8STVf+okvOenzizqPjFdnefmRVr0F683YkNriPjEYhZDQKBkfjn1gWOhHCdiJqmIgIuwBcQGtIzXDgVJILZ5KdSLvoSPpFB9DGL19HB83/t3evMXaNexzHvz9j2ul0Ql0n0pZW0nCOimtEVUTwwi3qMmImJHIS6RviEiJ4h3ghEZcIQlyCiFsR1QgRSpzzoqV6hDk9pVGXoZ22akolbu3fi+cZRs2YGWvv2V1r/z7Jzp619tq7z5P/dP9nPc9a/6elJWhpgY62bUydsp2O9u10tG+jY0p6bv3TN6WA6bCtk7Z1a2n/bBXtn73D5P4vaN3cz6Rv1tO65es/vCOAAabRTycb2Pf35+hkw6Z96d+UtpfTSX/rTLb+PMailaP44QeYXJuPGlE9E8lwOXDH05+Rjhlp/3CXNg17SiVpIbAwb26VtHqEdo7Zl/nxJnDbH1oQMLAxPdb2wn9eKvpP/ZW9gWZYxacZ+tkMfQT3c4iB/Bjl66jg9QJDtbUVevuYbmyrZyLpA2YO2Z4BfDXCMX15aGt3YPMo7x3tMwGIiAeAB/5u43dWkt4dy6lm2TVDP5uhj+B+NoN63rzwDjBH0mxJk4BuYPEOxywGLs4/dwFvRJq0WQx0S5osaTYwB1g+xs80M7MJVLczkjzncRnwKulS3YcjolfSTcC7EbEYeAh4XNIa0plId35vr6RngP8BvwCXRsQ2gOE+s159MDOz0TXFDYlVImlhHrartGboZzP0EdzPZuBEYmZmhbjAk5mZFeJEUiKSTpW0WtIaSdc1uj21IGmmpKWSVknqlXRF3r+npNckfZyf92h0W2tBUouklZKW5O3Zkpblfj6dLyIpLUnTJC2S9P8c03lVjKWkq/Lv64eSnpTUVrVYjocTSUnkkjP3AKcB/wR6cimZsvsFuDoi/gEcC1ya+3Ud8HpEzAFez9tVcAWwasj2rcAduZ/fkOrLldldwCsRcTBwGKmvlYqlpOnA5cDRETGXdOFPN9WL5Zg5kZTHMcCaiPgkIn4CngIWNLhNhUXEuoh4L//8HemLZzqpb4/mwx4Fzm5MC2tH0gzgDODBvC3gJGBRPqTU/ZS0G3AC6WpMIuKniBiggrEkXfE6Jd//1g6so0KxHC8nkvIYruTM9BGOLSVJs4AjgGVAZ0Ssg5RsgCqs+nUncC0wuHDGXsBARAyWqC17TA8ENgKP5OG7ByVNpWKxjIgvScUtPiclkC3ACqoVy3FxIimPsZScKS1JHcBzwJUR8W2j21Nrks4ENkTEiqG7hzm0zDHdFTgSuC8ijgC+p+TDWMPJczwLgNmk6uRTSUPOOypzLMfFiaQ8xlJyppQktZKSyBMR8Xze3S9pv/z6fsCGRrWvRuYDZ0n6lDQseRLpDGVaHh6B8se0D+iLiGV5exEpsVQtlqcAayNiY0T8DDwPHEe1YjkuTiTlUcnyMHme4CFgVUTcPuSloeVzLgZenOi21VJEXB8RMyJiFil2b0TEhcBSUnkgKHk/I2I98IWkg/Kuk0nVKSoVS9KQ1rGS2vPv72A/KxPL8fINiSUi6XTSX7GD5WFuaXCTCpN0PPA28AG/zx3cQJoneQbYn/Qf9/yI2NyQRtaYpBOBayLiTEkHks5Q9gRWAhdFxI+NbF8Rkg4nXUwwCfgE+BfpD9ZKxVLSjcAFpKsOVwKXkOZEKhPL8XAiMTOzQjy0ZWZmhTiRmJlZIU4kZmZWiBOJmZkV4kRiZmaFOJGY1ZGkcySFpIMb3RazenEiMauvHuDf5GWkzarIicSsTnL9sPmkcuLded8uku7Na1kskfSypK782lGS3pK0QtKrg2VFzHZ2TiRm9XM2aW2Oj4DNko4EzgVmAYeS7oaeB7/VG7sb6IqIo4CHgdJXLrDmsOvoh5jZ39RDKmkDqXRGD9AKPBsR24H1kpbm1w8C5gKvpfJNtJBKlJvt9JxIzOpA0l6kCr9zJQUpMQTwwkhvAXojYt4ENdGsZjy0ZVYfXcBjEXFARMyKiJnAWmATcF6eK+kETszHrwb2kfTbUJekQxrRcLPxciIxq48e/nz28RxpIaQ+4EPgflKV4y15+eQu4FZJ7wP/Ja1xYbbTc/VfswkmqSMitubhr+XA/LyWh1kpeY7EbOItkTSNtGbHzU4iVnY+IzEzs0I8R2JmZoU4kZiZWSFOJGZmVogTiZmZFeJEYmZmhTiRmJlZIb8CilQs+Ae/tnoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.kdeplot(train['Age'][(train['Survived']==0) & (train['Age'].notnull())],\n",
    "               color='Red', shade=True)\n",
    "g = sns.kdeplot(train['Age'][(train['Survived']==1) & (train['Age'].notnull())],\n",
    "               color='Blue', shade=True)\n",
    "g.set_xlabel('Age')\n",
    "g.set_ylabel('Frequency')\n",
    "g.legend(['Not survived', 'Survived'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['Fare'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['Fare'] = dataset['Fare'].fillna(dataset['Fare'].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Voyager\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.distplot(dataset['Fare'], color='m', label='Skewness : %.2f' % dataset['Fare'].skew())\n",
    "# g = g.legend(loc='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['Fare'] = dataset['Fare'].map(lambda i: np.log(i) if i > 0 else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Voyager\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x28f73836630>"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(dataset['Fare'], color='b')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Categorial values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.barplot(x='Sex', y='Survived', data=train)\n",
    "g = g.set_ylabel('Survived probability')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.747573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.190559</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Survived\n",
       "Sex             \n",
       "female  0.747573\n",
       "male    0.190559"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[['Sex', 'Survived']].groupby('Sex').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pclass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x28f73912588>"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.factorplot(x='Pclass', y='Survived', data=train, kind='bar', size=6, palette='muted')\n",
    "g.set_ylabels('survival probability')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x28f71c65278>"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 502.5x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.factorplot(x='Pclass', y='Survived', hue='Sex', data=train, size=6, kind='bar', palette='muted')\n",
    "g.set_ylabels('survival probability')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Emabarked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['Embarked'].isnull().sum()\n",
    "dataset['Embarked'].fillna('S', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S    906\n",
       "C    270\n",
       "Q    123\n",
       "Name: Embarked, dtype: int64"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['Embarked'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x28f739a1470>"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(x='Embarked', y='Survived', data=train, size=6, kind='bar', palette='muted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.factorplot('Pclass', col='Embarked', data=train, size=6, kind='count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.Filling missing Values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 Age "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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/pJmNAx4HhgMvAX/j7klf5hYRSbUuL5a4+0vAVcCHgS8BH3D3Ld3Yx53AjtOWvwc86O5FwFFAF15EJFLJXDW+lcTYXSVAMTDTzKYHk7Z3tW0h8AlgRbBswDXAL4NVKoFbela6iEhqJHNofAdwJfBssHw18Eeg2Mz+l7v/Wyfb/hD4n0Bbf54RwDF3b7u8WgOMaW9DM5sLzAUYO3ZsEmWKiPRMMh2qW4GJ7v4pd/8UMAloAqYCCzvayMw+CRx2902nP93Oqu12qHT35e5e5u5l+fkZdX+2iPQyybQIL3L3Q6ctHwaK3f1NM2vuZLuPADeZ2ceB/sBgEi3EoWaWG7QKC4Gk+/pkupqamh7PE3u+8++C5qmVzHLHV+aVHDl6vG+q3m/EsCEnHvnxQ7s6W2fWrFkXrV+/fsiIESNaqqqqtiX73skE4e/N7DfAE8Hyp4D/MLMLgGMdbeTu3wS+CWBmVwPfcPfZZvYEiavOjwPlwNPJFpvpGhsbeW3rS4wd1P1uln2bE43zd/e82KN9760/p1O+SKSOHD3ed997r01ZELLvt12u8oUvfKHuzjvvPPz5z3/+nJFsOpNMEH4NuBWYFixvAArc/R3gY93ZWWAh8LiZ3Q+8TIbeodJTYwed5Ntl9Wnf7/0bB6V9nyKZZsaMGfW7du3qdvh2GYTu7mb2JxLnBD8DvA482Z2duPtzwHPB17uBy7tbqIhIWDoMQjMrBm4DbgeOkLi/2Ny9J61AEZGM1VmLcCfwe2Cmu1cDmNldaalKRCSNOus+8yngIPCsmf1fM5tO+91fRESyWoctQnd/CngquDp8C3AXMNrMfgw85e5r01SjiGShEcOGnEjmSm+33q8LM2fOHPfHP/7xPUePHs0dPXr0pYsWLdp/11131XW1XTIXS94BHgMeM7PhwCxgEaAglLTQGJPZqas+f2FYtWrV6z3ZrltD9bv7m+6+zN2v6cnOREQyUTL9CEUipTEmJWyavElEYk9BKCKxp0NjkU5EOZCGBtFIHwWhSCeiGkhDg2ikl4JQpAtRDKTRGwbRWPDVO0rqj9WlbPSZQUNHnvjBPz/SaZec6urqvNmzZ4+rra3Ny8nJoby8vPaee+453NV7KwhFJBT1x+r6fnvC7pQF4f07u14nLy+PpUuX1kybNq3h6NGjOZdddtmkj3/842+Vlpa+29l2ulgiIr3G+973vuZp06Y1AAwbNqz14osvbty7d2+XYawWYS9xqCGHE1VVPTq5rtGxpTfatWtX3+3btw+86qqrujyvoSDsJd49aTR4PS+/8XL3N25NPPRoW+hknHKRaBw/fjzn1ltvvfiBBx7YN3z48Nau1lcQ9iZDofXqLn/mKZfznM6wSOZoamqyT3ziExfPmjXrzfLy8qQ+pkP7DTaz/ma2wcxeMbNtZnZv8Pw4M3vBzKrM7Odmlro5DUQk1lpbW7ntttveV1xc/O7ixYsPdb1FQpgtwibgGnevN7M84HkzWw0sAB5098fN7F9IzJv84xDrEJEIDBo68kQyV3q7835drbNu3bpBv/71r0cUFRU1TpgwYRLAvffe+8ZnP/vZ451tF1oQursDbScp84J/DlwDfC54vhJYTIqDMKfhzR4N2WTvvgWA9x/co33SP6/b24l0JNunh+2qz18Ybrjhhvqz5lJPSqjnCM2sD7AJGA/8CPgTcCyY0xigBhjTwbZzgbkAY8eOTXqf48eP73G9VVVvA1B0cU9GHLmQmpoaaNGVA0mNxsZGXt72Mgztwca6ANYtoQahu58EppjZUOApYGJ7q3Ww7XJgOUBZWVm767TnfD7B2ratqKjo0fa33norf367TyR3BTSdtL+0v6X30AWwtEjLd+vux0hM53kFMNTM2gK4ENifjhpERDoSWovQzPKBZnc/ZmYDgGuB7wHPAp8GHgfKgafDqiHdCgsLebflQCQTvP/ts0NoGKS5tUR6IsxD4wKgMjhPmAP8wt1/Y2bbgcfN7H7gZeCREGsQEelSmFeNtwCXtfP8buDysPYrkkpNTU38+d30n/f989t9aLYmGJjW3caW7iwRkVB88WtfLDly/EjKbpgYMWTEiRU/WtFpl5yGhgabOnXqhBMnTtjJkydt5syZRx988MEur0MoCEU60a9fP96b1xjJeIT7mvvRTHNa95tKR44f6Xt46uHU3Tn2Qter9O/f359//vldQ4YMaW1qarIPfehDJevXrz8+ffr0dzrbLl7XyEWkV8vJyWHIkCGtACdOnLCWlhYz6/oiooJQRHqVlpYWJkyYMGn06NEfvOqqq9665pprOm0NgoJQRHqZ3Nxcdu7cuX3v3r1bXnrppQtefPHF/l1toyAUkV5p5MiRJ6dNm/b2qlWrhnS1roJQRHqN/fv359bV1fUBqK+vt+eee27wxIkTO52vBHTVuNc40WpwLKJ7RI9BjdeEuguNKJR9RgwZcSKZK73der8u7Nu3L2/OnDnjTp48ibvbzTff/Obtt9/e6RBcoCCULKARhbJTV33+wjB16tTGHTt2bO/udgrCXqJvjtMy1CIbqaRwTGFo7x/liELz58/n3T0Herx/yQ46RygisacgFJFUaW1tbc3oIZCC+s45bNKhcYrtre/ZDfqHGhKfSaMH9uzQtulkRv/+STxsra2tnZSfn388Jycn6cGU06W1tdVqa2uHAFvPfk1BmELnc1L/RDDHRP+Linq0/YCqKuo1RLVEqKWl5YsHDx5ccfDgwclk5tFmK7C1paXli2e/oCBMoahP6vd4fgqRFCgtLT0M3BR1HT2hIBTJUE1NTXCi9/YNzSSZ2HwVEUkrtQhFMlS/fv1oHtjcK/uGZprQWoRm9l4ze9bMdpjZNjO7M3h+uJmtM7Oq4HFYWDWIiCQjzEPjFuDr7j6RxDSeXzOzScAiYL27FwHrg2URkciEOXnTAeBA8PXbZrYDGAPcDFwdrFZJYr7jhWHVESs9HXShrddNT+cnOkbiJ9tLRdE3dG99H6xftzeTHkrLOUIzu4jEjHYvAKODkMTdD5jZqA62mQvMBRg7dmw6ysxq5zcwQaIPY9GYnvVhZMz57T+TRdU3tBioqalR39A0CT0IzWwQ8CTw9+7+VjLzBwC4+3JgOUBZWVnG9VLPNFH2YezNou4bWvtGbY/3L8kLtfuMmeWRCMHH3P1XwdOHzKwgeL0AOBxmDSIiXQnzqrEBjwA73P0Hp720EigPvi4Hng6rBhGRZIR5aPwR4G+AV81sc/Dct4AHgF+Y2R3AXmBWiDWIiHQpzKvGzwMdnRCcHtZ+RUS6S7fYiUjsKQhFJPYUhCISexp0QSST6W6htFAQimQo3S2UPgpCkQylu4XSR+cIRST2FIQiEnsKQhGJPQWhiMSeglBEYk9BKCKxpyAUkdhTEIpI7CkIRST2FIQiEnsKQhGJvTDnLHnUzA6b2dbTnhtuZuvMrCp4HBbW/kVEkhVmi/AnwI1nPbcIWO/uRcD6YFlEJFKhBaG7/wfw5llP3wxUBl9XAreEtX8RkWSl+xzhaHc/ABA8jupoRTOba2YbzWxjba0muRaR8GTsxRJ3X+7uZe5elp+fH3U5ItKLpTsID5lZAUDweDjN+xcROUe6g3AlUB58XQ48neb9i4icI8zuMz8D/gsoMbMaM7sDeAC4zsyqgOuCZRGRSIU2Z4m7397BS9PD2qeISE9k7MUSEZF0URCKSOwpCEUk9hSEIhJ7CkIRiT0FoYjEnoJQRGJPQSgisacgFJHYUxCKSOwpCEUk9hSEIhJ7CkIRiT0FoYjEXmjDcElmqaiooLq6ut3XduzYQVNTE3PmzGHw4MHtrjN+/Hjmz58fZok90tn3BVBVVQXQae2Z+r1J+qhFKDQ1NQGwe/fuiCtJvQEDBjBgwICoy5AMpxZhTHTU4tmwYQObN28+tVxeXk5paWm6yjpvaslJKsQqCHUYda577rnnjOVvfetbrFmzJqJqsot+n3qPSILQzG4E/gnoA6xw94yYuyTsQ6jO/nCi+qNpbGzsdFl6Tofk2SPtQWhmfYAfkZi8qQZ40cxWuvv2sPedyZ+8+qPJPlH/PmXiB2u2iqJFeDlQ7e67AczsceBmIPQgjJp+6SRd9MHaPVEE4Rhg32nLNcDUs1cys7nAXICxY8emp7IYKigo4MCBA2csS3bQB2vqRNF9xtp5zs95wn25u5e5e1l+fn4ayoqn++6774zl7373uxFVIhKdKIKwBnjvacuFwP4I6hCguLj4VCuwoKCA8ePHR1yRSPpFEYQvAkVmNs7M+gK3ASsjqEMC9913HxdccIFagxJbaT9H6O4tZvZ3wBoS3Wcedfdt6a5D/qK4uJjVq1dHXYZIZCLpR+ju/w78exT7FhE5m+41FpHYUxCKSOwpCEUk9hSEIhJ75n5OX+aMY2a1wJ+jriMNRgJ1URchKRWnn2mdu98YdRE9kRVBGBdmttHdy6KuQ1JHP9PsoENjEYk9BaGIxJ6CMLMsj7oASTn9TLOAzhGKSOypRSgisacgFJHYUxBmMDO72sx+E3UdcWZm881sh5k9FtL7Lzazb4Tx3pK8WE3nKdIDXwVmuPvrURci4VGLMGRmdpGZ7TSzFWa21cweM7NrzewPZlZlZpcH//7TzF4OHkvaeZ8LzOxRM3sxWO/mKL6fODGzfwHeD6w0s7vb+/83szlm9mszW2Vmr5vZ35nZgmCdP5rZ8GC9vw22fcXMnjSzge3s72Ize8bMNpnZ781sQnq/4/hSEKbHeBLzOF8KTAA+B0wDvgF8C9gJ/LW7XwZ8B/jHdt7jbuD/ufuHgI8B/8fMLkhD7bHl7l8mMY3Ex4AL6Pj/fzKJn+nlwHeBhuBn+V/A/wjW+ZW7f8jdPwjsAO5oZ5fLgXnuXkrid+Ofw/nO5Gw6NE6P1939VQAz2wasd3c3s1eBi4AhQKWZFZGYyCqvnfe4HrjptPNJ/YGxJP6oJHwd/f8DPOvubwNvm9lxYFXw/KskPvwAJpvZ/cBQYBCJEdpPMbNBwIeBJ8xOzW/WL4xvRM6lIEyPptO+bj1tuZXEz+A+En9M/93MLgKea+c9DPiUu+8Kr0zpRLv//2Y2la5/vgA/AW5x91fMbA5w9VnvnwMcc/cpqS1bkqFD48wwBHgj+HpOB+usAeZZ0Fwws8vSUJf8xfn+/78HOGBmecDss19097eA181sVvD+ZmYfPM+aJUkKwszwv4ElZvYHEhNatec+EofMW8xsa7As6XO+///3AC8A60icE27PbOAOM3sF2Aboglia6BY7EYk9tQhFJPYUhCISewpCEYk9BaGIxJ6CUERiT0EooQjuzd1mZlvMbHPQ8VgkI+nOEkk5M7sS+CTwV+7eZGYjgb4RlyXSIbUIJQwFJOa4bQJw9zp3329mpWb2u2B0lTVmVmBmucGoLFcDmNkSM/tulMVL/KhDtaRcMIDA88BA4LfAz4H/BH4H3OzutWb2WeAGd/+CmX0A+CUwn8RdNlPd/UQ01Usc6dBYUs7d682sFPgoiSGrfg7cT2K4qnXB7bp9gAPB+tvM7N9IjNpypUJQ0k1BKKFw95MkRtF5Lhhu7GvANne/soNNLgGOAaPTU6HIX+gcoaScmZUEYyu2mUJi3MT84EIKZpYXHBJjZrcCI4C/BirMbGi6a5Z40zlCSbngsPghEoOQtgDVwFygEKggMexYLvBD4CkS5w+nu/s+M5sPlLp7eRS1SzwpCEUk9nRoLCKxpyAUkdhTEIpI7CkIRST2FIQiEnsKQhGJPQWhiMTe/wcELnl4FSPFDwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 330.375x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.factorplot(y='Age', x='Sex', data=dataset, kind='box')\n",
    "g = sns.factorplot(y='Age', x='Sex', hue='Pclass', data=dataset, kind='box')\n",
    "g = sns.factorplot(y='Age', x='Parch', data=dataset, kind='box')\n",
    "g = sns.factorplot(y='Age', x='SibSp', data=dataset, kind='box')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.heatmap(dataset[['Age', 'Sex', 'SibSp', 'Parch', 'Pclass']].corr(), cmap='BrBG', annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "index_NaN_age = list(dataset['Age'][dataset['Age'].isnull()].index)\n",
    "\n",
    "for i in index_NaN_age:\n",
    "    age_med = dataset['Age'].median()\n",
    "    age_pred = dataset['Age'][((dataset['SibSp'] == dataset.iloc[i]['SibSp']) \n",
    "                              & (dataset['Parch'] == dataset.iloc[i]['Parch'])\n",
    "                              & (dataset['Pclass'] == dataset.iloc[i]['Pclass']))].median()\n",
    "    if not np.isnan(age_pred):\n",
    "        dataset['Age'].iloc[i] = age_pred\n",
    "    else:\n",
    "        dataset['Age'].iloc[i] = age_med\n",
    "#     print(age_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Voyager\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3694: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  errors=errors)\n"
     ]
    }
   ],
   "source": [
    "train_age = train[train['Age'].notnull()]\n",
    "\n",
    "train_age.drop(labels=['PassengerId', 'Embarked', 'Ticket', 'Cabin'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.981001</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.266662</td>\n",
       "      <td>Mrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.070022</td>\n",
       "      <td>Miss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3.972177</td>\n",
       "      <td>Mrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.085672</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex   Age  SibSp  Parch      Fare  Title\n",
       "0         0       3    1  22.0      1      0  1.981001     Mr\n",
       "1         1       1    0  38.0      1      0  4.266662    Mrs\n",
       "2         1       3    0  26.0      0      0  2.070022   Miss\n",
       "3         1       1    0  35.0      1      0  3.972177    Mrs\n",
       "4         0       3    1  35.0      0      0  2.085672     Mr"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_age.head()\n",
    "# train_age['Sex'] = train_age['Sex'].map({'male':1, 'female':0})\n",
    "# train_age['Title'] = train_age['Name'].map(lambda i: i.split(',')[1].split('.')[0])\n",
    "# train_age.drop(labels=['Name'], axis=1, inplace=True)\n",
    "# train_age['Fare'] = train_age['Fare'].map(lambda i: np.log(i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_age['Fare'] = train_age['Fare'].map(lambda i: 0 if i <= 0 else i)\n",
    "# train_age = pd.get_dummies(train_age, columns=['Title'], prefix='Title')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Title_ Capt</th>\n",
       "      <th>Title_ Col</th>\n",
       "      <th>Title_ Don</th>\n",
       "      <th>...</th>\n",
       "      <th>Title_ Master</th>\n",
       "      <th>Title_ Miss</th>\n",
       "      <th>Title_ Mlle</th>\n",
       "      <th>Title_ Mme</th>\n",
       "      <th>Title_ Mr</th>\n",
       "      <th>Title_ Mrs</th>\n",
       "      <th>Title_ Ms</th>\n",
       "      <th>Title_ Rev</th>\n",
       "      <th>Title_ Sir</th>\n",
       "      <th>Title_ the Countess</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.981001</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.266662</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.070022</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3.972177</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.085672</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex   Age  SibSp  Parch      Fare  Title_ Capt  \\\n",
       "0         0       3    1  22.0      1      0  1.981001            0   \n",
       "1         1       1    0  38.0      1      0  4.266662            0   \n",
       "2         1       3    0  26.0      0      0  2.070022            0   \n",
       "3         1       1    0  35.0      1      0  3.972177            0   \n",
       "4         0       3    1  35.0      0      0  2.085672            0   \n",
       "\n",
       "   Title_ Col  Title_ Don         ...           Title_ Master  Title_ Miss  \\\n",
       "0           0           0         ...                       0            0   \n",
       "1           0           0         ...                       0            0   \n",
       "2           0           0         ...                       0            1   \n",
       "3           0           0         ...                       0            0   \n",
       "4           0           0         ...                       0            0   \n",
       "\n",
       "   Title_ Mlle  Title_ Mme  Title_ Mr  Title_ Mrs  Title_ Ms  Title_ Rev  \\\n",
       "0            0           0          1           0          0           0   \n",
       "1            0           0          0           1          0           0   \n",
       "2            0           0          0           0          0           0   \n",
       "3            0           0          0           1          0           0   \n",
       "4            0           0          1           0          0           0   \n",
       "\n",
       "   Title_ Sir  Title_ the Countess  \n",
       "0           0                    0  \n",
       "1           0                    0  \n",
       "2           0                    0  \n",
       "3           0                    0  \n",
       "4           0                    0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_age_y = train_age['Age']\n",
    "train_age_x = train_age.drop(['Age'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "568 568 143 143\n"
     ]
    }
   ],
   "source": [
    "# train_age_y\n",
    "# train_age_x\n",
    "# train_num = int(len(train_age_y)*0.8) \n",
    "# train_age_x_train = train_age_x[:train_num]\n",
    "# train_age_y_train = train_age_y[:train_num]\n",
    "# train_age_x_test = train_age_x[train_num:]\n",
    "# train_age_y_test = train_age_y[train_num:]\n",
    "# print(len(train_age_x_train), len(train_age_y_train), len(train_age_x_test), len(train_age_y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.kernel_ridge import KernelRidge \n",
    "\n",
    "clf = KernelRidge(alpha=1)\n",
    "clf = clf.fit(train_age_x, train_age_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
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       "      <td>18</td>\n",
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       "      <td>Williams, Mr. Charles Eugene</td>\n",
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       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Masselmani, Mrs. Fatima</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Emir, Mr. Farred Chehab</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0</td>\n",
       "      <td>2631</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>O'Dwyer, Miss. Ellen \"Nellie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330959</td>\n",
       "      <td>7.8792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Todoroff, Mr. Lalio</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349216</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Spencer, Mrs. William Augustus (Marie Eugenie)</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17569</td>\n",
       "      <td>146.5208</td>\n",
       "      <td>B78</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Glynn, Miss. Mary Agatha</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>335677</td>\n",
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       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Mamee, Mr. Hanna</td>\n",
       "      <td>male</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2677</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Kraeff, Mr. Theodor</td>\n",
       "      <td>male</td>\n",
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       "      <td>349253</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Rogers, Mr. William John</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S.C./A.4. 23567</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Lennon, Mr. Denis</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>370371</td>\n",
       "      <td>15.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>O'Driscoll, Miss. Bridget</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14311</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Samaan, Mr. Youssef</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
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       "      <td>2662</td>\n",
       "      <td>21.6792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Woolner, Mr. Hugh</td>\n",
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       "      <td>C52</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Stewart, Mr. Albert A</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17605</td>\n",
       "      <td>27.7208</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Moubarek, Master. Gerios</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2661</td>\n",
       "      <td>15.2458</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>77</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Staneff, Mr. Ivan</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349208</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moutal, Mr. Rahamin Haim</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>374746</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>83</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>McDermott, Miss. Brigdet Delia</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330932</td>\n",
       "      <td>7.7875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>88</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Slocovski, Mr. Selman Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/OQ 392086</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>96</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Shorney, Mr. Charles Joseph</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>374910</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>102</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Petroff, Mr. Pastcho (\"Pentcho\")</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349215</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Moss, Mr. Albert Johan</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>312991</td>\n",
       "      <td>7.7750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>110</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Miss. Bertha</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>371110</td>\n",
       "      <td>24.1500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>122</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moore, Mr. Leonard Charles</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A4. 54510</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>127</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>McMahon, Mr. Martin</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370372</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>129</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Peter, Miss. Anna</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2668</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>F E69</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Boulos, Mrs. Joseph (Sultana)</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2678</td>\n",
       "      <td>15.2458</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>155</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Olsen, Mr. Ole Martin</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Fa 265302</td>\n",
       "      <td>7.3125</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>690</th>\n",
       "      <td>698</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Mullens, Miss. Katherine \"Katie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35852</td>\n",
       "      <td>7.7333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>702</th>\n",
       "      <td>710</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Moubarek, Master. Halim Gonios (\"William George\")</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2661</td>\n",
       "      <td>15.2458</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>704</th>\n",
       "      <td>712</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Klaber, Mr. Herman</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113028</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>C124</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>711</th>\n",
       "      <td>719</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>McEvoy, Mr. Michael</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36568</td>\n",
       "      <td>15.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>720</th>\n",
       "      <td>728</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Mannion, Miss. Margareth</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36866</td>\n",
       "      <td>7.7375</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>725</th>\n",
       "      <td>733</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Knight, Mr. Robert J</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239855</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>731</th>\n",
       "      <td>739</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Ivanoff, Mr. Kanio</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349201</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>732</th>\n",
       "      <td>740</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Nankoff, Mr. Minko</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349218</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>733</th>\n",
       "      <td>741</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Hawksford, Mr. Walter James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16988</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>D45</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>761</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Garfirth, Mr. John</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>358585</td>\n",
       "      <td>14.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>759</th>\n",
       "      <td>767</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Brewe, Dr. Arthur Jackson</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112379</td>\n",
       "      <td>39.6000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>761</th>\n",
       "      <td>769</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. Daniel J</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>371110</td>\n",
       "      <td>24.1500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>774</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Elias, Mr. Dibo</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2674</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>769</th>\n",
       "      <td>777</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Tobin, Mr. Roger</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>383121</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>F38</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>771</th>\n",
       "      <td>779</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Kilgannon, Mr. Thomas J</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36865</td>\n",
       "      <td>7.7375</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>776</th>\n",
       "      <td>784</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Mr. Andrew G</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>783</th>\n",
       "      <td>791</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Keane, Mr. Andrew \"Andy\"</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12460</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785</th>\n",
       "      <td>794</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Hoyt, Mr. William Fisher</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17600</td>\n",
       "      <td>30.6958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>807</th>\n",
       "      <td>816</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Fry, Mr. Richard</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112058</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>B102</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>817</th>\n",
       "      <td>826</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Flynn, Mr. John</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>368323</td>\n",
       "      <td>6.9500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>818</th>\n",
       "      <td>827</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Lam, Mr. Len</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1601</td>\n",
       "      <td>56.4958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>820</th>\n",
       "      <td>829</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>McCormack, Mr. Thomas Joseph</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>367228</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>824</th>\n",
       "      <td>833</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Saad, Mr. Amin</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2671</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>829</th>\n",
       "      <td>838</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sirota, Mr. Maurice</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>392092</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>831</th>\n",
       "      <td>840</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Marechal, Mr. Pierre</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11774</td>\n",
       "      <td>29.7000</td>\n",
       "      <td>C47</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>840</th>\n",
       "      <td>850</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Goldenberg, Mrs. Samuel L (Edwiga Grabowska)</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>17453</td>\n",
       "      <td>89.1042</td>\n",
       "      <td>C92</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>850</th>\n",
       "      <td>860</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Razi, Mr. Raihed</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2629</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>858</th>\n",
       "      <td>869</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>van Melkebeke, Mr. Philemon</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>345777</td>\n",
       "      <td>9.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>879</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Laleff, Mr. Kristo</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349217</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>170 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "5              6         0       3   \n",
       "17            18         1       2   \n",
       "19            20         1       3   \n",
       "26            27         0       3   \n",
       "27            29         1       3   \n",
       "28            30         0       3   \n",
       "30            32         1       1   \n",
       "31            33         1       3   \n",
       "35            37         1       3   \n",
       "41            43         0       3   \n",
       "44            46         0       3   \n",
       "45            47         0       3   \n",
       "46            48         1       3   \n",
       "47            49         0       3   \n",
       "54            56         1       1   \n",
       "63            65         0       1   \n",
       "64            66         1       3   \n",
       "75            77         0       3   \n",
       "76            78         0       3   \n",
       "81            83         1       3   \n",
       "86            88         0       3   \n",
       "93            96         0       3   \n",
       "99           102         0       3   \n",
       "105          108         1       3   \n",
       "107          110         1       3   \n",
       "119          122         0       3   \n",
       "124          127         0       3   \n",
       "126          129         1       3   \n",
       "138          141         0       3   \n",
       "152          155         0       3   \n",
       "..           ...       ...     ...   \n",
       "690          698         1       3   \n",
       "702          710         1       3   \n",
       "704          712         0       1   \n",
       "711          719         0       3   \n",
       "720          728         1       3   \n",
       "725          733         0       2   \n",
       "731          739         0       3   \n",
       "732          740         0       3   \n",
       "733          741         1       1   \n",
       "753          761         0       3   \n",
       "759          767         0       1   \n",
       "761          769         0       3   \n",
       "766          774         0       3   \n",
       "769          777         0       3   \n",
       "771          779         0       3   \n",
       "776          784         0       3   \n",
       "783          791         0       3   \n",
       "785          794         0       1   \n",
       "807          816         0       1   \n",
       "817          826         0       3   \n",
       "818          827         0       3   \n",
       "820          829         1       3   \n",
       "824          833         0       3   \n",
       "829          838         0       3   \n",
       "831          840         1       1   \n",
       "840          850         1       1   \n",
       "850          860         0       3   \n",
       "858          869         0       3   \n",
       "868          879         0       3   \n",
       "878          889         0       3   \n",
       "\n",
       "                                                  Name     Sex  Age  SibSp  \\\n",
       "5                                     Moran, Mr. James    male  NaN      0   \n",
       "17                        Williams, Mr. Charles Eugene    male  NaN      0   \n",
       "19                             Masselmani, Mrs. Fatima  female  NaN      0   \n",
       "26                             Emir, Mr. Farred Chehab    male  NaN      0   \n",
       "27                       O'Dwyer, Miss. Ellen \"Nellie\"  female  NaN      0   \n",
       "28                                 Todoroff, Mr. Lalio    male  NaN      0   \n",
       "30      Spencer, Mrs. William Augustus (Marie Eugenie)  female  NaN      1   \n",
       "31                            Glynn, Miss. Mary Agatha  female  NaN      0   \n",
       "35                                    Mamee, Mr. Hanna    male  NaN      0   \n",
       "41                                 Kraeff, Mr. Theodor    male  NaN      0   \n",
       "44                            Rogers, Mr. William John    male  NaN      0   \n",
       "45                                   Lennon, Mr. Denis    male  NaN      1   \n",
       "46                           O'Driscoll, Miss. Bridget  female  NaN      0   \n",
       "47                                 Samaan, Mr. Youssef    male  NaN      2   \n",
       "54                                   Woolner, Mr. Hugh    male  NaN      0   \n",
       "63                               Stewart, Mr. Albert A    male  NaN      0   \n",
       "64                            Moubarek, Master. Gerios    male  NaN      1   \n",
       "75                                   Staneff, Mr. Ivan    male  NaN      0   \n",
       "76                            Moutal, Mr. Rahamin Haim    male  NaN      0   \n",
       "81                      McDermott, Miss. Brigdet Delia  female  NaN      0   \n",
       "86                       Slocovski, Mr. Selman Francis    male  NaN      0   \n",
       "93                         Shorney, Mr. Charles Joseph    male  NaN      0   \n",
       "99                    Petroff, Mr. Pastcho (\"Pentcho\")    male  NaN      0   \n",
       "105                             Moss, Mr. Albert Johan    male  NaN      0   \n",
       "107                                Moran, Miss. Bertha  female  NaN      1   \n",
       "119                         Moore, Mr. Leonard Charles    male  NaN      0   \n",
       "124                                McMahon, Mr. Martin    male  NaN      0   \n",
       "126                                  Peter, Miss. Anna  female  NaN      1   \n",
       "138                      Boulos, Mrs. Joseph (Sultana)  female  NaN      0   \n",
       "152                              Olsen, Mr. Ole Martin    male  NaN      0   \n",
       "..                                                 ...     ...  ...    ...   \n",
       "690                   Mullens, Miss. Katherine \"Katie\"  female  NaN      0   \n",
       "702  Moubarek, Master. Halim Gonios (\"William George\")    male  NaN      1   \n",
       "704                                 Klaber, Mr. Herman    male  NaN      0   \n",
       "711                                McEvoy, Mr. Michael    male  NaN      0   \n",
       "720                           Mannion, Miss. Margareth  female  NaN      0   \n",
       "725                               Knight, Mr. Robert J    male  NaN      0   \n",
       "731                                 Ivanoff, Mr. Kanio    male  NaN      0   \n",
       "732                                 Nankoff, Mr. Minko    male  NaN      0   \n",
       "733                        Hawksford, Mr. Walter James    male  NaN      0   \n",
       "753                                 Garfirth, Mr. John    male  NaN      0   \n",
       "759                          Brewe, Dr. Arthur Jackson    male  NaN      0   \n",
       "761                                Moran, Mr. Daniel J    male  NaN      1   \n",
       "766                                    Elias, Mr. Dibo    male  NaN      0   \n",
       "769                                   Tobin, Mr. Roger    male  NaN      0   \n",
       "771                            Kilgannon, Mr. Thomas J    male  NaN      0   \n",
       "776                             Johnston, Mr. Andrew G    male  NaN      1   \n",
       "783                           Keane, Mr. Andrew \"Andy\"    male  NaN      0   \n",
       "785                           Hoyt, Mr. William Fisher    male  NaN      0   \n",
       "807                                   Fry, Mr. Richard    male  NaN      0   \n",
       "817                                    Flynn, Mr. John    male  NaN      0   \n",
       "818                                       Lam, Mr. Len    male  NaN      0   \n",
       "820                       McCormack, Mr. Thomas Joseph    male  NaN      0   \n",
       "824                                     Saad, Mr. Amin    male  NaN      0   \n",
       "829                                Sirota, Mr. Maurice    male  NaN      0   \n",
       "831                               Marechal, Mr. Pierre    male  NaN      0   \n",
       "840       Goldenberg, Mrs. Samuel L (Edwiga Grabowska)  female  NaN      1   \n",
       "850                                   Razi, Mr. Raihed    male  NaN      0   \n",
       "858                        van Melkebeke, Mr. Philemon    male  NaN      0   \n",
       "868                                 Laleff, Mr. Kristo    male  NaN      0   \n",
       "878           Johnston, Miss. Catherine Helen \"Carrie\"  female  NaN      1   \n",
       "\n",
       "     Parch           Ticket      Fare  Cabin Embarked  \n",
       "5        0           330877    8.4583    NaN        Q  \n",
       "17       0           244373   13.0000    NaN        S  \n",
       "19       0             2649    7.2250    NaN        C  \n",
       "26       0             2631    7.2250    NaN        C  \n",
       "27       0           330959    7.8792    NaN        Q  \n",
       "28       0           349216    7.8958    NaN        S  \n",
       "30       0         PC 17569  146.5208    B78        C  \n",
       "31       0           335677    7.7500    NaN        Q  \n",
       "35       0             2677    7.2292    NaN        C  \n",
       "41       0           349253    7.8958    NaN        C  \n",
       "44       0  S.C./A.4. 23567    8.0500    NaN        S  \n",
       "45       0           370371   15.5000    NaN        Q  \n",
       "46       0            14311    7.7500    NaN        Q  \n",
       "47       0             2662   21.6792    NaN        C  \n",
       "54       0            19947   35.5000    C52        S  \n",
       "63       0         PC 17605   27.7208    NaN        C  \n",
       "64       1             2661   15.2458    NaN        C  \n",
       "75       0           349208    7.8958    NaN        S  \n",
       "76       0           374746    8.0500    NaN        S  \n",
       "81       0           330932    7.7875    NaN        Q  \n",
       "86       0  SOTON/OQ 392086    8.0500    NaN        S  \n",
       "93       0           374910    8.0500    NaN        S  \n",
       "99       0           349215    7.8958    NaN        S  \n",
       "105      0           312991    7.7750    NaN        S  \n",
       "107      0           371110   24.1500    NaN        Q  \n",
       "119      0        A4. 54510    8.0500    NaN        S  \n",
       "124      0           370372    7.7500    NaN        Q  \n",
       "126      1             2668   22.3583  F E69        C  \n",
       "138      2             2678   15.2458    NaN        C  \n",
       "152      0        Fa 265302    7.3125    NaN        S  \n",
       "..     ...              ...       ...    ...      ...  \n",
       "690      0            35852    7.7333    NaN        Q  \n",
       "702      1             2661   15.2458    NaN        C  \n",
       "704      0           113028   26.5500   C124        S  \n",
       "711      0            36568   15.5000    NaN        Q  \n",
       "720      0            36866    7.7375    NaN        Q  \n",
       "725      0           239855    0.0000    NaN        S  \n",
       "731      0           349201    7.8958    NaN        S  \n",
       "732      0           349218    7.8958    NaN        S  \n",
       "733      0            16988   30.0000    D45        S  \n",
       "753      0           358585   14.5000    NaN        S  \n",
       "759      0           112379   39.6000    NaN        C  \n",
       "761      0           371110   24.1500    NaN        Q  \n",
       "766      0             2674    7.2250    NaN        C  \n",
       "769      0           383121    7.7500    F38        Q  \n",
       "771      0            36865    7.7375    NaN        Q  \n",
       "776      2       W./C. 6607   23.4500    NaN        S  \n",
       "783      0            12460    7.7500    NaN        Q  \n",
       "785      0         PC 17600   30.6958    NaN        C  \n",
       "807      0           112058    0.0000   B102        S  \n",
       "817      0           368323    6.9500    NaN        Q  \n",
       "818      0             1601   56.4958    NaN        S  \n",
       "820      0           367228    7.7500    NaN        Q  \n",
       "824      0             2671    7.2292    NaN        C  \n",
       "829      0           392092    8.0500    NaN        S  \n",
       "831      0            11774   29.7000    C47        C  \n",
       "840      0            17453   89.1042    C92        C  \n",
       "850      0             2629    7.2292    NaN        C  \n",
       "858      0           345777    9.5000    NaN        S  \n",
       "868      0           349217    7.8958    NaN        S  \n",
       "878      2       W./C. 6607   23.4500    NaN        S  \n",
       "\n",
       "[170 rows x 12 columns]"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_age = train[train['Age'].isnull()]\n",
    "test_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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opZg/73OOyfSTmmRsiE+IzsxesGCBodcxgw6YGHv//fdpamzEn3usodcJZg0CVwqvvPqqodfpqjZt2kRZeQUn9DCu9dIixxumb2qY+fPnGX6teNMBE0ONjY288cabBNPzCCd3N/ZiNjvNPY9h/bp1rF692thrdUELFy7ELjCmgxMbW+v47s1s3Lgp4YYf6ICJoQ8++IC6ulp8vUbH5XqB7KHg9PLiSy/F5XpdyeJFCxnWLUCywbdHLcZEB/F99dVXcblevOiAiZGmpiZemzmTUHpvwilx2kvO5qC550jWrF6tWzExVFJSQlFxSdxaLwB5ySGyvSrhJrPqgImR999/n7raWpp7HRfX6wZ6DIu0Yl58Ma7XTWQtW7x2dN2XthCB4zKb+WblyoSazKoDJgYirZfX49t6adHSilmzJrJyntZhixctondKmGxPbGZOt9bo7gH8gQArV66M63WNpAMmBiKtl5q4t15a7G/F6L6YDqutrWXtunWMzYp/K+KobgG8SSTUbZIOmA5qbm7m9TfeIJTWK/6tlxY2B809j2H1qlVdYhlGIy1evJhwOLy/0zWeHDY4NtPH4kULE2YaiA6YDpo9ezY11dVxe3J0KIHsYUiSm5dfecXUOjq7+fPnke1RDEg1Zu7RkYzv4aOmto5VCbLAuw6YDggEAsyc+Tqh1BxCqTnmFmNPornH0axYvpz8/Hxza+mkKisrWbFiJeN7NMdyAnybjMwM4EkiYVa60wHTAXPnzqWiohyfwaN2W8vfYzjicDFjxgyzS+mU5syZQzgc5pQc857iOO1wYo/Ier11dXWm1RErOmDaKRQK8drMmajkLEId3DEgZhxOmrOH8eXChRQXF5tdTacSDAb58IP3Oapb7Nbdba/To+v1zpkzx9Q6YkEHTDstXryYXSUlNPccGdMFpToq0PNoEBtvvPGG2aV0KgsWLGDP3n2cnWfc0gyt1T81xNBuQd568w0CgYDZ5XSIDph2UErx2mszwZ1GMLO/2eV8h0ry4M8awpxPPkm4eS1GCYVCvDbjVXolq5ivu9teF/VrpKy8otP3xRwxYESkp4g8LyIfR98eISI/N74061q7di1btmymOdpasBp/zjGEQiHeeecds0vpFD755BMKt+/g0v712CzSGD02M8Cg9BAv/Os5GhsbzS6n3Vrz0/ES8AnQK/p2PnCLUQV1BjNnvo44PQS6DzG7lINS7jQC3frxn/feS9i1XmOloaGBfz03ncHpIcbHYWmG1hKBnw6up6KqmpkzZ5pdTru1JmC6K6XeAsIASqkgYM4gAQsoLCxk6dIlNGcPB5vD7HIOyZ8zkqbGxoResT4Wnn32Waqqq/nZkDordaUBMCQ9yEk9fbz++kwKCgrMLqddWhMwDSKSBSgAEZkA1BhalYXNnDkTsSfh7zHc7FIOK5ySTSgtlzfefAu/3zq/ma1kxYoVzJo1i/P6NDEwzZq/M68e2kCyI8wjj/ypU3b4tiZg/g/4ABgkIouBV4DftPYCImIXkVUiMiv69gARWSoiW0XkTRGJ/arYBikpKWHevHn4ug8Dh8vsco7IlzuK6qpKPv74Y7NLsZyKigr++NCD9EpW/HiAdfs4UpMU1w2tpaBgG9OnTze7nDY7YsAopb4BTgNOAm4EjlZKrW3DNW4GNh3w9qPAE0qpIUAV0Gk6jGfMmIHChj/3GLNLaZVQai7hlB68OmNGp/ztZ5RgMMhDDz1IQ10tvz66Bqfd7IoOb2x2gLPymnj77bdZuHCh2eW0SWueIv0YuBgYBgwFLhKRM0XkiDP7RCQPuAD4V/RtASYB/44e8jJwaftKj6+SkhI+/fRTfNlDUUles8tpHRGae42mvKyMjz76yOxqLOOf//wnq1at5pqhdeSlWPPW6PuuGtzIwLQQD//xIQoLC80up9Vac4v0cyIBcXX0z3NEbpsWi8j/O8Jr/wbcQbSDGMgCqqMdxQAlwEGHwYrIDSKyQkRWlJWVtaJMY73wwgsoseHPHRXT87qKlmBvrMDeWIFn80e4imK7sX0orTfh1J68+NLLCbWQUXvNmjWLf//735yT18QpubHfrdEoSTb435G1uPAx9e67qK6uNrukVmlNwISB4Uqpy5VSlwMjAB8wHrjzUC8SkQuBUqXUgavnHKyf/qCLniqlpiulximlxmVnZ7eiTONs3ryZefPm0dxjBCrJE9Nz2xorkVAACQVw1O3F1lgZ0/MjQnPvsVRXVfL222/H9tydzPLly3niiccZmRngqsHW7Xc5lExXmJuPqaGivJSpU+/uFL8wWhMw/ZVS+w54uxQYqpSqBA53Y38ycLGI7ADeIHJr9Degm4i0PN/NA3a3ueo4Ukrx1NNPI06P4VuRGCWUmkMwox8zZrzWZUf35ufn8/vf3Utvb5BfH1OH3XrjI1tlUFqQm0bUsWnjJh544AHLrxvTmk/zQhGZJSLXiMg1wPvAlyKSDByynaaUulsplaeU6g9MAeYppa4G5gNXRA9rOZ9lffbZZ6xft46mXmMM2QY2XprzjscXCPDss8+aXUrc7dq1izvv+C1e8XHbsdV4HJ17N8xx2X5+NrSBr776iieeeMLSu3u2JmD+B3gRGB39swxQSqkGpdQZ7bjmncD/iUgBkT6Z59txjrioq6vjqaefJpySTaD7ULPL6RDlTsOXM5LPPvuMb775xuxy4qaiooLb/+9WAo01/PbYKjJc1v1hbIuz85q5uF8js2fP5vnnLfsj1KrH1ArYRuR26DLgTL772PmIlFJfKKUujP67UCl1glJqsFLqSqWUZXvann76aWpqamjqe6KlZky3lz/3WHCn8eif/0xTk/mzho1WV1fHb2+/jcqKUm47toZeJi/DEGuXD2zijF7NzJgxw7L9a4cMGBEZKiK/F5FNwFNAMSBKqTOUUk/FrUKTLFmyhDlz5uDLGWn8Lo3xYnPQ2H8i+/bu7ZSDttrC54s8bdm5cwc3H1PLoDTj+ypm5HvZWWdnZ52dh79JY0a+scMZROCaYQ0cn+3n6aeftuTM68O1YDYTaa1cpJSaqJR6ki4yB6myspKH//QIypuJ36SdAowSSs3B33ME//nPf1i6dKnZ5RgiFArxwAP3s379Bn45vI5jMuMzyLCo3kFTyEZTyMbm6iSK6o2fq2YTuHFEHcMzgjz66CMsW7bM8Gu2xeEC5nJgLzBfRJ4TkTM5+GPmhBIOh3nkkUeoq6unccBpYLP4MM928OWNQ3kz+ePDf0q4p0pKKZ588kkWL/6Kq4c0ML5n4s/DctrhlpG19PYG+cPvf8fWrVvNLmm/QwaMUuo/SqnJwFHAF8CtQE8ReVZEzolTfXE3Y8YMli1bRlOf4wl7M8wuxxg2B40DT6OuvoH77rvf8o862+Ltt9/mvffe47y+TZzTx/rjRGLF41Dcdmw1XvFx1x2/pbS01OySgNZ18jYopV6LdtLmAauBuwyvzARLly7lhRdfJJA1iED2UWaXY6iwJ4PGfiexbt1apk2bZnY5MbFs2TL++eyzHJ/tY/KgzjeQrqMyXIrbRlbTUFfN7+69B5/P/OcnbRpupJSqVEpNU0pNMqogsxQVFXHf/fejPBk09zspIZ4aHUkwaxD+HsN5++23O/0C07t27eKB+++jd0qIG0ZYZ2W6eMtLCXHj8Fq25G/lr3/9q+ljZDrpeMbYqq2t5a6776Y5qGgYfBbYk8wuKW58fcYTSsvlL4891ml3hQwEAjxw/32E/Y3cckwNrsTrNmuTsdkBLu3fyKeffsonn3xiai1dPmD8fj/33Hsvu/fspWHgGShXitklxZfNRuOgSYSSkrl76lRKSkrMrqjNXnjhBbbkb+Xnw+rivmG9VV06oIlh3YL87YknTP2adumAUUrxl7/8hXVr19LUf6L5uzOaxeGifvBZNDQHuOOOOzvNTF2IzDF68403OC23meMttKau2WwCN42owxb28de/PmbarVKXDpjnn3+euXPn4us9hmDWILPLMZVyp9MwaBJ79u5l6lRrdBAeSTgc5vHH/0qKUzGlE86ONlqmO8wVA+pZtWo1n3/+uSk1dNmA+fDDD5kxYwb+7KExX+Olswql5tA44DQ2btzAQw89RChk7XGVCxYsYPPmLUwZWEdyUmLMMYq1Sb19DEgLMX3aP01Z1bBLBsxXX33F448/TjA9D18XeWLUWsHM/jT3Gc/ChQt55plnzC7nkMLhMK+8/BK9khUn5ehbo0OxCfx4QAOlZeWmTCXocgGzZcsW7rvvfkLeLJoGnWHJjdPMFsg5Gn/Po3nnnXcsO4lu5cqVbN+xk4v6dd1H0q11bGaAAWkh3nrj9bj3xXSpn67S0lLuvOtu/JJE45Cu9Ti6rXx9TiCY0Y+nn3mGRYsWmV3OD3zyySd4k+AE3bF7RCJwem4TO4tLyM/Pj+u1u0zANDU1ceddd1FTV0/D4LM7z8LdZhGhacBphJO788ADD1pq4y+/38/ChV8yPruZpC7zHdwxJ/Tw47DBvHnz4nrdLvHlUUrxyCOPsH37dhoGnp64c4xize6gcfCZ+MXB3VOnWubx9ZYtW/D5/IzM0luxtFZykmJQWpDVq1fF9bpdImDeeustFixYQHPvcYTS88wup1NRSV4aBk6ivLyCBx54wBJPljZs2ADA0HQdMG0xNN3P1q0FcR2CkPABs2HDBqZNm04wox+BnM6xYZrVhFOyaeo7gW+++YbXXnvN7HLYtWsXqS4hzakfTbdFr+QQ4XCYvXv3xu2aCR0wjY2N3P/Ag4SdXpr6T9SPozsg0H0ogcyBvPjii2za1KYVU2Nu3759ZLnMb0l1Nt3dkWkU8VzKIaEDZvr06ZTu20tj/1Osu5d0yI/b7eaKK67A7XZDyKJPRURo7ncSypnMww//ydSRvj6fD7fNegHTFJTvfC2bgtb6heayR1p8+hYpBjZv3sx7772Hv8cIS88xkqCfCy+8kF//+tdccMEFSNCiAQPgcNLY7ySKi4t48803TS3FijdHjUH5ztey0WIB0yKeY2GMXzTUBJFlE59CnB58eWPMLuewlMPJrFmzUEoxe/ZslMPaj89D6XkEM/rx2mszOf/88+nePf4Lonu9XqpD1luTwetQ3/la9rTY/kstLSqvN37fYwnZglm2bBkbNqynKfc462+WZnfS3NzMO++8E9kK1Or1Et3Eze/n9ddfN+X62dnZVPisFzAeh/rO19JqG7xV+iI/7j169IjbNRMyYN54401wJRPoPsTsUhKScqcRyBzIrFmzqa2tjfv1e/XqRb1fUeu35i2IVe1usGO323TAdMTu3btZteobfN2PSsgdAazCn3M0Pl9z3EeGAgwfPhyAwtqEvMM3zLbaJAYOGIjLFb8HHgkXMAsXLgQgkDXQ5EoSW9iTCZ5uLPjyy7hfe+jQoSQ57Gyo0nPJWssXgm21To4ZOTKu1024gFm1ahXKm4FypZpdSmITwZ/Wm7Vr18Z92xOPx8OYMWNZVeHGwvu+W8qGyiR8IcXJJ58c1+smXMBs37GDoLub2WV0CSFvJqFgMK4jQ1uccuqplDYK2/RtUqt8tc9Fakoyo0bFd3G1hAuY6qpqPVM6Tlo+z5WVlXG/9qRJk/C4XczbZdEBlBZS4xdWlrn40bnnkZQU39vKhAuYJGcSKOuN8kxI0c+z0xn/R+ter5dzfnQuS0vd+x+/agc3t8RNSMHFF18c92sn3Feme1Z37M3xf3TaFdmin+fMzExTrj9lyhTCYmPWDo8p1+8M6gLC3BIvp512Kn379o379RMuYMaOHYOjoRTCibPfslU5anfTq3fvuI6rOFBubi7nnXc+8/e42duYcN/KMfHBDg/NIbj22utMuX7CfVVOOeUUVChIUukWs0tJaLamKhy1uzjt1FNNreO6667D5XLzan6KfqL0PUX1duaWeLjgggsZMGCAKTUkXMCMHj2a444bg2ffWgg2m11OYlIKV/EK3G43kydPNrWUrKws/vvnv2BdZRJf77P+NIt4CYXhxS2ppKSkcP3115tWh2EBIyJ9RGS+iGwSkQ0icnP0/ZkiMldEtkb/jvn6lb/61U3YwgG8274ApbcSjTXn3nU4aoq57tpr6dbN/CEBl156KSNGDOeVramUNyfc78x2+XAfj0tGAAAV0UlEQVSnh201dm6+5VbS09NNq8PIr0YQuE0pNRyYAPyPiIwA7gI+V0oNAT6Pvh1TQ4YM4fbbbsNeuxvXzq/RbefYcVRux1WygjPOOIOf/OQnZpcDgMPh4N57f4eyu5i2MZVgF/+dsqXawXs7vJx11lmceeaZptZiWMAopfYopb6J/rsO2AT0Bi4BXo4e9jJwqRHXP++887j66qtxlm3Bve0LCOtH1x2VVLoJz7b5jBhxNHfccQdioRUCe/XqxW23/5Yt1Q5eL+i646Aqm208tSGd3NxcbrnlFrPLiU8fjIj0B44DlgI9lVJ7IBJCwEEfQYjIDSKyQkRWlJWVteu6119/Pb/85S9JqtqON/8TxN/QrvMYKezNRNmTUPYkgqk5hL3mPPI9rHAIV9FS3Du/ZsKECTz++F/xeKz3aPiss87iJz/5CXNLPHyxu+sNwPOF4B/r0/CLiz8+/CdSUlLMLsn4BadEJAV4B7hFKVXb2t96SqnpwHSAcePGtfseZ8qUKXTv3p0///kvODa+T0P/iYS6xX88wKH4+k7A1hgZCdt01PkmV/ND0lyDt3ABtoZyLrnkEn7zm9/gcFh3eP4NN9zA9u2FvLRiBd2cYUZ37xo7D4TC8NT6VHbUOXjwod/Tv39/s0sCDG7BiEgSkXB5TSn1bvTd+0QkN/rxXMDwFYjPOuss/vWv5xjQtzferZ/h2r4IguatKdspqDBJ+zaQuvEDUmnmwQcf5NZbb7V0uECkP+b++x9g8ODBPLUhja011q43FsIKXtiSzJoKJzffckvcJzQejpFPkQR4HtiklHr8gA99AFwT/fc1wPtG1XCgvn378s9nn+Wqq67CXVlA2vp3cJRv1R3AB2FrKCd50yzcRUsZO2Y0L774AqeccorZZbWa1+vl0T//heyeuTy2Np3C2sRdF0gpeCU/mYV73FxzzTVccsklZpf0HUa2YE4G/h8wSURWR/+cDzwCnC0iW4Gzo2/HhdPp5MYbb+S5555j2OABeLYvJHnLx9gayuNVgqVJoAnXjsUkb/qQTGeI3//+9/zlz382baRuR2RkZPDE3/5Ot8we/HlNN3bUGR8yfVOCeOxhPPYwR3UL0DfF2NHkSsFrW73M2+Xmqquu4tprrzX0eu0h8VxhvL3GjRunVqxYEdNzhsNhPvroI6ZNf4662hoCWYPx5Y1FOZNjep3W8Gz+CDCxDyYcxLlvA+6965BwiMsuu5TrrrvOEp2EHbV3715u/t/fUFdVxm9H1TAozdgf+oe/SQNg6hhj58OFFbyan8znu9xceeWV/OpXv4r3U71WXazLjkqy2WxceOGFvPH6zMhtU80OUte9g7N4Rdfpn1FhHOVbSV3/Lq6SlZx4wjhefvklfvOb3yREuADk5OTw9388SXpWT/68Op0t1Z2/Tyas4MXNkXCZMmWKGeHSal02YFokJydz4403MuPVV5l0xmm49q4lbf07JO1dn7gTJpXCXl1MysYP8GxfyJC+uTz++OM8/PDD9OnTx+zqYi4nJ4d/PPkU3XN689iadNZXdt6lNoNh+OfGFBbscfNf//Vf3HjjjZYNF9ABs19ubi6/+93veO655xgz6hjcxctIXf8ujvKChOoIttWX4t3yMd6tc8lNd/GHP/yBadOmMWaMtfeP6qjs7Gz+/o8n6d2nP4+vTWNlWecLGX8o8ih6yT4X119/Pf/93/9t6XABHTA/MGTIEP762GM89thjDO6Ti2f7l6Rs+gB7TYnZpXWINNfiLphH8qZZZEgTN998MzNefZUzzjgDm61rfBtkZmbyt3/8g8FDhvLk+jSWdqLJkb4Q/G1dGt+UO7n55pu5+uqrzS6pVTr/DalBxo0bx/TpY5g/fz7Tpj9Haf6nBNPz8PU5gbDH/Al+rRb04dq9BmfZRlxJTqZccw2TJ0+O6+5+VpKWlsbjT/yNu+68g2fWryeo6jg5x8Lb9RIJl8fXprO52sGdd97JeeedZ3ZJrdY1fnW1k81m48wzz2TGq69w0003keqvJHnDe7iKlli/I1iFSSrdTNqGd3GWbuD8c89l5szXuO6667psuLRoGSczevRopm9M5au91m3JtITLluokpk69p1OFC+iAaRWn08nkyZOZOfM1LrzgfFylm0jb8B8cldst2T9ja6wiefNHuHd+xdHDBjN92jTuuOMOsrKyzC7NMjweD4888iijjxvN9E2pLC+1XsgEwvCPdWlsrnZwz733cvbZZ5tdUpvpgGmDjIwMbr/9dqZPn86gfr3xbJuPp+Bz60yiDIdwlqwkeeP7pNuaueeee3jyH/9g6NChZldmSS6Xiz/+8WGGDx/BMxtT2VhpnR6DsIJpG1NYV5nEHXfcafqyC+2lA6YdhgwZwj+ffZabbroJb+M+Uje+j726yNSapLmWlM2zce1Zwzlnn8Wrr7zC2WefbfmnDGbzer088uij5OX14ckN6ZZZ2/fd7R6Wlbr45S9/2eluiw5kjc9mJ+RwOJg8eTIvvPA8A/vmRSZRFi2FcPxXO3JUFpK66QNSaOahhx5i6tSpllhprrNITU3lT488isOTyhPruuEzeemgZaVOPtjh5fzzzzd9SdKO0gHTQXl5eTz77DNcdtllOPdtwFPwGYTitESAUjh3r8Gz7QuGDx3CCy88z8SJE+Nz7QTTq1cv7rv/AfY2Cm8WxH+6SItqn/BSfipHHTWMW2+9tdO3QHXAxIDTGRmbcNttt5FUu4vk/DnGLziuFK6ipbh2reTMM8/k73//Gz179jT2mgnuuOOO4/LLr+CzXW42VZnTH/Nyfgp+FXliFO9dGI2gAyaGLrroIh588EGczdUkF3xm6FQD565vcJZu5Morr+SeexLjm9EKrr/+enpkd+etwvhvg1JQ42BlmZNrrr3WlE3SjKADJsYmTpzIvffeg62+DHfhl4Y8xk4q24JrzxouuOACfvWrX3WZkbjx4HK5uPpn/49tNXY2VMU3tN/f4SEtNYXLLrssrtc1kv7ONMDpp5/OL2+8kaSqHSSVxXYDOFtTNe6iJYwdNy4h7tGt6LzzziMtNYXFe+O3rm+tX1hb4eSSSy9LqIGQOmAMMnnyZMaMGYOnZDniq4/NSZXCs2MRyR4PU+++2/LLV3ZWTqeTE8ZPYG2li3CcbpPWViShIOE66XXAGEREuOOOO3DYwLl7VUzO6ajaga2+lF//+n/0qFyDjR07ljo/7IvTuJiC2iRSk70MGTIkLteLFx0wBsrJyeHiiy/GWbEN8dV17GRK4d6zhrw+fTjnnHNiU6B2SLm5uQBU+OKznm9Fs42eubkJ15+WWP8bC5o8eTKCIqksv0PnsdWXIo2V/PSqq7DbE3cRa6vIyIjsaFzrj08fV13ATmZm4rVKdcAYrEePHhw3ZgyuqsIOPVFKqthGktPJ6aefHrvitEMKhSLDeR1x+gmxi9p/zUSiAyYOzpw0CZrrsDVVt+8ESuGqLeHECRMS6gmDlTU3RwZKOm3x6eVNsoVpamyMy7XiSQdMHBx//PEA2Gt3tev1tuYalK9+/3k047VsV5zhis/csgxXmLIyw/cgjDsdMHHQo0cPcnJysdfta9fr7fWR140ePTqWZWmHsW9f5HPe3R2fgOnuDlNRUUkwmFgLzeuAiZNjjjkaZ1P7Nniz1ZeSnJJKXl5ejKvSDqWkpIRkp5CcFJ9bpB6eMGGl2LNnT1yuFy86YOLkqKOOQvkaEH/b77OTmioZftQwPWo3jnbt2kVPd/xaEzneSAdvSUnnXlz++3TAxEnLqnK2xoq2vTAcQhqr9Kp0cVa6bw9ZrvgFTFa0r6el7ydR6ICJk8GDBwNgb2PA2JqqQIV1wMRZRUUl3eLUwQuQ5gwjQEVFG38BWZwOmDjxer306t0bW0Pb+mHs0eMTbQi51fn8ftz2+K3X4LBBkh18PovvVtFGOmDiaMTw4TibKto04M7WUEZKaiq9evUysDLNCqy3P0XH6YCJoxEjRkQ7elu/C0FSYzkjho/QHbxxluz1UheI34+HLwSBUGSv9ESiAyaORo4cCYC9bm+rjpdAM9JYxahRxxpZlnYQffr0ZV9j/OZ87Y1eq0+fPnG7ZjzogImjQYMG4U1Oxl7XurEOLUE0atQoI8vSDmLQ4MFsr3cSiFM/79aayNo+AwcOjM8F40QHTBzZbDbGjR2Ls25Pq/ph7DUleDxejjrqqDhUpx3opJNOojmo2FgZn2UzV5a7yOvdS7dgYkFEzhWRLSJSICJ3mVGDWcaPHw+++sjj58NRCmfdLsaNG6tXrjPBmDFjSEn2smCP8ctmljbZ2FSVxOlnTEq4vra4B4yI2IGngfOAEcBVIjIi3nWYZcKECYgIjgN2ggx7Mwl7M79znK2hHHwNCbeEYmfhdDr58eVXsKLMxa4GY/tiZu/0YLfbE2qx7xZmtGBOAAqUUoVKKT/wBnCJCXWYIisri+HDR+Cs3rn/fb6+E/D1nfCd4xxVO7HZbJx44onxLlGL+vGPf4zb5eLtbcYtkbG7wcaXe92cd/4FCbkMqhkB0xsoPuDtkuj7uowzzjgdaahAmmsOfoBSuKp3MGbsWNLS0uJbnLZft27d+K9rruGbcierylvXF9M3JUjflNZNMVAKXs5PxePxcu2113agUusyI2AOdpP5gx5PEblBRFaIyIpEm59x+umngwhJFYUH/bitsQKaa5l0xhnxLUz7gSuvvJJ+ffvwcn4aDYEj94/8bGgjPxvaugmtX+5xsanKwfU33EhmZuaRX9AJmREwJcCBXeV5wO7vH6SUmq6UGqeUGpednR234uIhOzubkSNH4qzaftCnSUkVhdjtdk455RQTqtMOlJSUxF13T6Xab+Pl/NgNgtvXaGNGQQqjR43ioosuitl5rcaMgFkODBGRASLiBKYAH5hQh6nOnDQJaar+4dMkpXBW7+CEE8aTmppqTnHadwwfPpxrr72WJftcLNrj7PD5gmF4dmMaSS4vU++5J+F2EjhQ3P9nSqkg8GvgE2AT8JZSakO86zDbqaeeGnmaVLXjO++3NZSBr55Jk/TtkZX89Kc/5dhjR/Ly1jR2N3Tsx+atbV4Ka+3c/ts76NGjR4wqtCZTolMp9ZFSaqhSapBS6o9m1GC2zMxMRo0aheuAp0kASZXbsTscnHTSSSZVph2Mw+Hgd7/7PW5vCk9vSMffzg0AVpUnMafYw6WXXtoldohI3LZZJzBx4kRorPr2aZJSOGuKGTNmTMJNeksE2dnZTL3nXorrbcwsaPvXp7LZxnOb0xg8aCA33XSTARVajw4YE5188skAOKojyyRKcy001zIx+n7NesaPH8+UKVOYt8vN8tLW98eEFTy7MZWguPjDfffjchk/QtgKdMCYKDc3l9zcXjhqIw/RHNFtTcaNG2dmWdoR/OIXv2DY0CG8mJ9Kta91Q/s/LnKzpdrBLbfemnDzjQ5HB4zJxo0bS1LDPlBh7HV7ye7Rg969u9S4w07H4XAw9Z578SkHL25JOeK81V0Ndt7ZnszEk0/mRz/6UXyKtAgdMCYbOXIkKujH1lSNs7GMUcfqtV86g379+vGLX1zPqnIn3xxmlK9S8NKWFDzeZG67/faEm8x4JDpgTNayFIOjphjla2DYsGEmV6S11uWXX86A/v2YUZCG7xBPlb7e52RLtYMbbvwlGRkZ8S3QAnTAmKx3794kJTlxRKcNDBo0yOSKtNZyOBz87823UNEE83a5f/DxYBje2Z7CkMGDOf/8802o0Hw6YExmt9vJ65OHPTqit1+/fiZXpLXFcccdx9gxY5hdnPyDVszivS7KmoSf/+IX2O3xW37TSnTAWECf6JawSU5nwk56S2TXXHsttb5IoLRQCj4uTmbokMGRRca6KB0wFtAyXDw7O7vLdQImgpEjRzJwQH8W7PHsf19+jYPdDcJlP768S39NdcBYQEurJV2v/dIpiQgXXnQx22vt+1e/W7THhdfj7hLTAQ5HB4wFnHzyyZx88slcccUVZpeitVPL0hqry5MIK1hT5eaE8RPweDxHeGVi06tJW0C/fv344x+75JzPhJGdnc3gQQNZW5nPMZkBqpvRy52iWzCaFjOjRh9HYa2D/JrIwLtj9aBJHTCaFivDhw/HF4JX85Pplp5GTk6O2SWZTt8iaVqMTJw4keuvvx6fz8fRRx/dpZ8etdABo2kx4na7ufrqq80uw1L0LZKmaYbRAaNpmmF0wGiaZhgdMJqmGUYHjKZphtEBo2maYXTAaJpmGB0wmqYZRtSRlkS3ABEpA3Ye8cDOrTtQbnYRWkx0ha9luVLq3CMd1CkCpisQkRVKKb0hUgLQX8tv6VskTdMMowNG0zTD6ICxjulmF6DFjP5aRuk+GE3TDKNbMJqmGUYHjKZphtEBYwEicq6IbBGRAhG5y+x6tPYRkRdEpFRE1ptdi1XogDGZiNiBp4HzgBHAVSIywtyqtHZ6CTji4LOuRAeM+U4ACpRShUopP/AGcInJNWntoJT6Eqg0uw4r0QFjvt5A8QFvl0Tfp2mdng4Y8x1s6Xk9dkBLCDpgzFcC9Dng7Txgt0m1aFpM6YAx33JgiIgMEBEnMAX4wOSaNC0mdMCYTCkVBH4NfAJsAt5SSm0wtyqtPUTkdeBrYJiIlIjIz82uyWx6qoCmaYbRLRhN0wyjA0bTNMPogNE0zTA6YDRNM4wOGE3TDKMDRjskEblHRDaIyFoRWS0i42NwzotjNWNcROpjcR7NOPoxtXZQInIi8DhwulLKJyLdAadS6oijjEXEER3fY3SN9UqpFKOvo7WfbsFoh5JLZO8bH4BSqlwptVtEdkTDBhEZJyJfRP99n4hMF5FPgVdEZKmIHN1yMhH5QkTGisi1IvKUiKRHz2WLftwrIsUikiQig0RkjoisFJGFInJU9JgBIvK1iCwXkQfj/PnQ2kEHjHYonwJ9RCRfRJ4RkdNa8ZqxwCVKqZ8SWXbiJwAikgv0UkqtbDlQKVUDrAFaznsR8IlSKkBk0ezfKKXGArcDz0SP+TvwrFLqeGBvh/+HmuF0wGgHpZSqJxIYNwBlwJsicu0RXvaBUqop+u+3gCuj//4J8PZBjn8TmBz995ToNVKAk4C3RWQ1MI1IawrgZOD16L9fbdN/SDOFw+wCNOtSSoWAL4AvRGQdcA0Q5NtfTO7vvaThgNfuEpEKETmWSIjceJBLfAD8SUQyiYTZPCAZqFZKjT5UWe3872gm0C0Y7aBEZJiIDDngXaOJ7A++g0gYAFx+hNO8AdwBpCul1n3/g9FW0jIitz6zlFIhpVQtsF1ErozWISIyKvqSxURaOgBXt/1/pcWbDhjtUFKAl0Vko4isJbJe8H3A/cDfRWQhEDrCOf5NJBDeOswxbwI/i/7d4mrg5yKyBtjAt0uI3gz8j4gsB9Lb9t/RzKAfU2uaZhjdgtE0zTA6YDRNM4wOGE3TDKMDRtM0w+iA0TTNMDpgNE0zjA4YTdMM8/8B44XqKTeOfewAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.factorplot(x='Survived', y='Age', data=train, kind='box')\n",
    "g = sns.factorplot(x='Survived', y='Age', data=train, kind='violin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    541\n",
       "1    340\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Survived'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.Feature engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1 Name/Title "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_title = [i.split(',')[1].split('.')[0].strip() for i in dataset['Name']]\n",
    "dataset['Title'] = pd.Series(dataset_title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.countplot(x='Title', data=dataset)\n",
    "g = plt.setp(g.get_xticklabels(), rotation=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['Title'] = dataset['Title'].replace(['Don', 'Rev', 'Dr', 'Mme', 'Ms',\n",
    "       'Major', 'Lady', 'Sir', 'Mlle', 'Col', 'Capt', 'the Countess',\n",
    "       'Jonkheer', 'Dona'], 'Rare')\n",
    "dataset['Title'] = dataset['Title'].map({'Master':0, 'Miss':1, 'Mrs':2, 'Mr':3, 'Rare':5})\n",
    "dataset['Title'] = dataset['Title'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.countplot(dataset['Title'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.factorplot(x='Title', y='Survived', data=dataset, kind='bar')\n",
    "g = g.set_xticklabels(['Master', 'Miss', 'Mrs', 'Mr', 'Rare'])\n",
    "g = g.set_ylabels('survival probability')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Title</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.589744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.706215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.792000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.157895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.444444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Survived\n",
       "Title          \n",
       "0      0.589744\n",
       "1      0.706215\n",
       "2      0.792000\n",
       "3      0.157895\n",
       "5      0.444444"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[['Title', 'Survived']].groupby('Title').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.drop(labels=['Name'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2 Family size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['Fsize'] = dataset['SibSp'] + dataset['Parch'] + 1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.factorplot(x='Fsize', y='Survived', data=dataset, kind='bar')\n",
    "g = g.set_ylabels('Survival probability')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.get_dummies(dataset, columns=['Title'])\n",
    "dataset = pd.get_dummies(dataset, columns=['Embarked'], prefix='Em')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.3 Cabin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     292\n",
       "unique    186\n",
       "top        G6\n",
       "freq        5\n",
       "Name: Cabin, dtype: object"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['Cabin'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1      C85\n",
       "3     C123\n",
       "6      E46\n",
       "10      G6\n",
       "11    C103\n",
       "Name: Cabin, dtype: object"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['Cabin'][dataset['Cabin'].notnull()].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['Cabin'] = pd.Series([i[0] if not pd.isnull(i) else 'X' for i in dataset['Cabin']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['X', 'C', 'E', 'G', 'D', 'A', 'B', 'F', 'T'], dtype=object)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['Cabin'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.countplot(dataset['Cabin'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.factorplot(x='Cabin', y='Survived', data=dataset, kind='bar')\n",
    "g = g.set_ylabels('Survival probability')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.get_dummies(dataset, columns=['Cabin'], prefix='Cabin')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.4 Ticket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fsize</th>\n",
       "      <th>...</th>\n",
       "      <th>Em_S</th>\n",
       "      <th>Cabin_A</th>\n",
       "      <th>Cabin_B</th>\n",
       "      <th>Cabin_C</th>\n",
       "      <th>Cabin_D</th>\n",
       "      <th>Cabin_E</th>\n",
       "      <th>Cabin_F</th>\n",
       "      <th>Cabin_G</th>\n",
       "      <th>Cabin_T</th>\n",
       "      <th>Cabin_X</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>1.981001</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>4.266662</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>2.070022</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>3.972177</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>113803</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>2.085672</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>373450</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age      Fare  Parch  PassengerId  Pclass     Sex  SibSp  Survived  \\\n",
       "0  22.0  1.981001      0            1       3    male      1       0.0   \n",
       "1  38.0  4.266662      0            2       1  female      1       1.0   \n",
       "2  26.0  2.070022      0            3       3  female      0       1.0   \n",
       "3  35.0  3.972177      0            4       1  female      1       1.0   \n",
       "4  35.0  2.085672      0            5       3    male      0       0.0   \n",
       "\n",
       "             Ticket  Fsize   ...     Em_S  Cabin_A  Cabin_B  Cabin_C  Cabin_D  \\\n",
       "0         A/5 21171      2   ...        1        0        0        0        0   \n",
       "1          PC 17599      2   ...        0        0        0        1        0   \n",
       "2  STON/O2. 3101282      1   ...        1        0        0        0        0   \n",
       "3            113803      2   ...        1        0        0        1        0   \n",
       "4            373450      1   ...        1        0        0        0        0   \n",
       "\n",
       "   Cabin_E  Cabin_F  Cabin_G  Cabin_T  Cabin_X  \n",
       "0        0        0        0        0        1  \n",
       "1        0        0        0        0        0  \n",
       "2        0        0        0        0        1  \n",
       "3        0        0        0        0        0  \n",
       "4        0        0        0        0        1  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['Pclass'] = dataset['Pclass'].astype('category')\n",
    "dataset = pd.get_dummies(dataset, columns=['Pclass'], prefix='Pc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.drop(labels=['PassengerId'], axis=1, inplace=True)\n",
    "dataset.drop(labels=['Ticket'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "dataset['Sex'] = dataset['Sex'].map({'male':1, 'female':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Fsize</th>\n",
       "      <th>Title_0</th>\n",
       "      <th>Title_1</th>\n",
       "      <th>Title_2</th>\n",
       "      <th>...</th>\n",
       "      <th>Cabin_C</th>\n",
       "      <th>Cabin_D</th>\n",
       "      <th>Cabin_E</th>\n",
       "      <th>Cabin_F</th>\n",
       "      <th>Cabin_G</th>\n",
       "      <th>Cabin_T</th>\n",
       "      <th>Cabin_X</th>\n",
       "      <th>Pc_1</th>\n",
       "      <th>Pc_2</th>\n",
       "      <th>Pc_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>38.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age      Fare  Parch  Sex  SibSp  Survived  Fsize  Title_0  Title_1  \\\n",
       "0  22.0  1.981001      0    1      1       0.0      2        0        0   \n",
       "1  38.0  4.266662      0    0      1       1.0      2        0        0   \n",
       "2  26.0  2.070022      0    0      0       1.0      1        0        1   \n",
       "3  35.0  3.972177      0    0      1       1.0      2        0        0   \n",
       "4  35.0  2.085672      0    1      0       0.0      1        0        0   \n",
       "\n",
       "   Title_2  ...   Cabin_C  Cabin_D  Cabin_E  Cabin_F  Cabin_G  Cabin_T  \\\n",
       "0        0  ...         0        0        0        0        0        0   \n",
       "1        1  ...         1        0        0        0        0        0   \n",
       "2        0  ...         0        0        0        0        0        0   \n",
       "3        1  ...         1        0        0        0        0        0   \n",
       "4        0  ...         0        0        0        0        0        0   \n",
       "\n",
       "   Cabin_X  Pc_1  Pc_2  Pc_3  \n",
       "0        1     0     0     1  \n",
       "1        0     1     0     0  \n",
       "2        1     0     0     1  \n",
       "3        0     1     0     0  \n",
       "4        1     0     0     1  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Voyager\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3694: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  errors=errors)\n"
     ]
    }
   ],
   "source": [
    "# Separate train and test dataset \n",
    "train = dataset[:train_len]\n",
    "test = dataset[train_len:]\n",
    "test.drop(labels=['Survived'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Voyager\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "train['Survived'] = train['Survived'].astype(int)\n",
    "Y_train  = train['Survived']\n",
    "X_train = train.drop(labels=['Survived'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.1 Simple modeling "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6.1.1 Cross validate models \n",
    "Compared 10 popular classifiers and evaluate the mean accuracy of each of them by a stratified kfold cross validation procedure \n",
    "* SVC\n",
    "* Decision \n",
    "* AdaBoost \n",
    "* RandomForest \n",
    "* Extra Trees \n",
    "* Gradient Boosting \n",
    "* Multiple layer perceprton(neural network)\n",
    "* KNN \n",
    "* Logistic Regression \n",
    "* Linear Discriminant Analysis "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "kfold = StratifiedKFold(n_splits=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# clf = GradientBoostingClassifier(random_state=random_state)\n",
    "# cross_val_score(clf, X_train, Y_train, scoring='accuracy', cv=kfold, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Cross validation scores')"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "random_state = 42 \n",
    "classifiers = []\n",
    "classifiers.append(SVC(random_state=random_state))\n",
    "classifiers.append(DecisionTreeClassifier(random_state=random_state))\n",
    "classifiers.append(AdaBoostClassifier(DecisionTreeClassifier(random_state=random_state), random_state=random_state, learning_rate=0.1))\n",
    "classifiers.append(RandomForestClassifier(random_state=random_state))\n",
    "classifiers.append(ExtraTreesClassifier(random_state=random_state))\n",
    "classifiers.append(GradientBoostingClassifier(random_state=random_state))\n",
    "classifiers.append(MLPClassifier(random_state=random_state))\n",
    "classifiers.append(KNeighborsClassifier())\n",
    "classifiers.append(LogisticRegression(random_state=random_state))\n",
    "classifiers.append(LinearDiscriminantAnalysis())\n",
    "\n",
    "cv_results = []\n",
    "for classifier in classifiers:\n",
    "    cv_results.append(cross_val_score(classifier, X_train, Y_train, scoring='accuracy', cv=kfold, n_jobs=4))\n",
    "    \n",
    "cv_means = []\n",
    "cv_std = []\n",
    "for cv_result in cv_results:\n",
    "    cv_means.append(cv_result.mean())\n",
    "    cv_std.append(cv_result.std())\n",
    "\n",
    "cv_res = pd.DataFrame({'CrossValMeans':cv_means, 'CrossValerrors': cv_std,\n",
    "                      'Algorithm':['SVC', 'DecisionTree', 'AdaBoost', 'RandomForest', 'ExtraTrees',\n",
    "                                  'GradientBoosting', 'MultipleLayerPerceptron', 'KNeighboors', \n",
    "                                  'LogisticRegression', 'LinearDiscriminantAnalysis']})\n",
    "g = sns.barplot('CrossValMeans', 'Algorithm', data=cv_res, palette='Set3', orient='h', **{'xerr':cv_std})\n",
    "g.set_xlabel('Mean Accuracy')\n",
    "g.set_title('Cross validation scores')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CrossValMeans</th>\n",
       "      <th>CrossValerrors</th>\n",
       "      <th>Algorithm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.822932</td>\n",
       "      <td>0.045495</td>\n",
       "      <td>SVC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.790066</td>\n",
       "      <td>0.047433</td>\n",
       "      <td>DecisionTree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.795761</td>\n",
       "      <td>0.046135</td>\n",
       "      <td>AdaBoost</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.819561</td>\n",
       "      <td>0.028208</td>\n",
       "      <td>RandomForest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.794574</td>\n",
       "      <td>0.031346</td>\n",
       "      <td>ExtraTrees</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.832010</td>\n",
       "      <td>0.044819</td>\n",
       "      <td>GradientBoosting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.830912</td>\n",
       "      <td>0.042471</td>\n",
       "      <td>MultipleLayerPerceptron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.799157</td>\n",
       "      <td>0.045656</td>\n",
       "      <td>KNeighboors</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.826353</td>\n",
       "      <td>0.034371</td>\n",
       "      <td>LogisticRegression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.825204</td>\n",
       "      <td>0.032977</td>\n",
       "      <td>LinearDiscriminantAnalysis</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CrossValMeans  CrossValerrors                   Algorithm\n",
       "0       0.822932        0.045495                         SVC\n",
       "1       0.790066        0.047433                DecisionTree\n",
       "2       0.795761        0.046135                    AdaBoost\n",
       "3       0.819561        0.028208                RandomForest\n",
       "4       0.794574        0.031346                  ExtraTrees\n",
       "5       0.832010        0.044819            GradientBoosting\n",
       "6       0.830912        0.042471     MultipleLayerPerceptron\n",
       "7       0.799157        0.045656                 KNeighboors\n",
       "8       0.826353        0.034371          LogisticRegression\n",
       "9       0.825204        0.032977  LinearDiscriminantAnalysis"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6.1.2 Hyperparameter tunning for best models \n",
    "Grid search optimization for SVC, RandomForest, GradientBoosting, multipleLayerPerceptron, LogisticRegression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 54 candidates, totalling 540 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  46 tasks      | elapsed:   18.3s\n",
      "[Parallel(n_jobs=-1)]: Done 196 tasks      | elapsed:  1.0min\n",
      "[Parallel(n_jobs=-1)]: Done 446 tasks      | elapsed:  2.5min\n",
      "[Parallel(n_jobs=-1)]: Done 540 out of 540 | elapsed:  3.1min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8376844494892168"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# RandomForest\n",
    "RFC = RandomForestClassifier()\n",
    "\n",
    "rf_param_grid = {\n",
    "    'max_depth': [None],\n",
    "    'max_features': [1,3,10],\n",
    "    'min_samples_split': [2,3,10],\n",
    "    'min_samples_leaf':[1,3,10],\n",
    "    'bootstrap': [False],\n",
    "    'n_estimators': [100, 300],\n",
    "    'criterion': ['gini']\n",
    "}\n",
    "\n",
    "gsRFC = GridSearchCV(RFC, param_grid=rf_param_grid, cv=kfold, \n",
    "                     scoring='accuracy', n_jobs=-1, verbose=1)\n",
    "gsRFC.fit(X_train, Y_train)\n",
    "RFC_best = gsRFC.best_estimator_\n",
    "gsRFC.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train['Age'] = X_train['Age'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 72 candidates, totalling 720 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  46 tasks      | elapsed:    8.5s\n",
      "[Parallel(n_jobs=-1)]: Done 196 tasks      | elapsed:   26.4s\n",
      "[Parallel(n_jobs=-1)]: Done 446 tasks      | elapsed:   53.7s\n",
      "[Parallel(n_jobs=-1)]: Done 720 out of 720 | elapsed:  1.4min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8217934165720772"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# GradientBoosting \n",
    "GBC = GradientBoostingClassifier()\n",
    "gb_param_grid = {\n",
    "    'loss': ['deviance'],\n",
    "    'n_estimators': [100, 200, 300],\n",
    "    'learning_rate': [0.1, 0.05, 0.01],\n",
    "    'max_depth':[4,8],\n",
    "    'min_samples_leaf':[100, 150],\n",
    "    'max_features': [0.3, 0.1]\n",
    "}\n",
    "gsGBC = GridSearchCV(GBC, param_grid=gb_param_grid, cv=kfold, scoring='accuracy',\n",
    "                    n_jobs=-1, verbose=1)\n",
    "gsGBC.fit(X_train, Y_train)\n",
    "GBC_best = gsGBC.best_estimator_\n",
    "gsGBC.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 28 candidates, totalling 280 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  46 tasks      | elapsed:    9.2s\n",
      "[Parallel(n_jobs=-1)]: Done 196 tasks      | elapsed:   38.2s\n",
      "[Parallel(n_jobs=-1)]: Done 280 out of 280 | elapsed:  1.2min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8320090805902384"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# SVC \n",
    "SVMC = SVC(probability=True)\n",
    "svc_param_grid = {\n",
    "    'kernel': ['rbf'],\n",
    "    'gamma': [0.001, 0.01, 0.1, 1],\n",
    "    'C': [1, 10, 50, 100, 200, 300, 1000]\n",
    "}\n",
    "gsSVMC = GridSearchCV(SVMC, param_grid=svc_param_grid, cv=kfold,\n",
    "                     scoring='accuracy', n_jobs=-1, verbose=True)\n",
    "gsSVMC.fit(X_train, Y_train)\n",
    "SVMC_best = gsSVMC.best_estimator_\n",
    "gsSVMC.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6.1.3 Plot learn curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\n",
    "                       n_jobs=-1, train_sizes=np.linspace(.1, 1, 5)):\n",
    "    \"\"\"Generate a simple plot os the best and training learning curve\"\"\"\n",
    "    plt.figure()\n",
    "    plt.title(title)\n",
    "    if ylim is not None:\n",
    "        plt.ylim(*ylim)\n",
    "    plt.xlabel('Training examples')\n",
    "    plt.ylabel('Score')\n",
    "    train_sizes, train_scores, test_scores = learning_curve(estimator, X, y, cv=cv,\n",
    "                                                           n_jobs=n_jobs, train_sizes=train_sizes)\n",
    "    train_scores_mean = np.mean(train_scores, axis=1)\n",
    "    train_scores_std = np.std(train_scores, axis=1)\n",
    "    test_scores_mean = np.mean(test_scores, axis=1)\n",
    "    test_scores_std = np.std(test_scores, axis=1)\n",
    "    plt.grid()\n",
    "    \n",
    "    plt.fill_between(train_sizes, train_scores_mean-train_scores_std, \n",
    "                    train_scores_mean+ train_scores_std, alpha=0.1, color='r')\n",
    "    plt.fill_between(train_sizes, test_scores_mean-test_scores_std,\n",
    "                    test_scores_mean + test_scores_std, alpha=0.1, color='g')\n",
    "    plt.plot(train_sizes, train_scores_mean, 'o-', color='r', label='Training Score')\n",
    "    plt.plot(train_sizes, test_scores_mean, 'o-', color='g', label='Cross-validation score')\n",
    "    plt.legend(loc='best')\n",
    "    return plt \n",
    "\n",
    "\n",
    "g = plot_learning_curve(gsRFC.best_estimator_, 'RF learning curves', X_train, Y_train, cv=kfold)\n",
    "g = plot_learning_curve(gsSVMC.best_estimator_, 'SVC learning curves', X_train, Y_train, cv=kfold)\n",
    "g = plot_learning_curve(gsGBC.best_estimator_, 'GradientBoosting learning curves', \n",
    "                       X_train, Y_train, cv=kfold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6.1.4 Feature importance of tree based classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nrows =1; ncols = 2 \n",
    "fig, axes = plt.subplots(nrows=nrows, ncols=ncols, sharex='all', figsize=(10, 10))\n",
    "\n",
    "name_classifiers = [('RandomForest', RFC_best), ('GradientBoosting', GBC_best)]\n",
    "nclassifier = 0 \n",
    "for row in range(nrows):\n",
    "    for col in range(ncols):\n",
    "        name = name_classifiers[nclassifier][0]\n",
    "        classifier = name_classifiers[nclassifier][1]\n",
    "        indices = np.argsort(classifier.feature_importances_)[::-1][:40]\n",
    "        g = sns.barplot(y=X_train.columns[indices][:40], \n",
    "                        x=classifier.feature_importances_[indices][:40], \n",
    "                       orient='h', ax=axes[col])\n",
    "        g.set_xlabel('Relative import ', fontsize=12)\n",
    "        g.set_ylabel('Features', fontsize=12)\n",
    "        g.tick_params(labelsize=9)\n",
    "        g.set_title(name + 'feature importance')\n",
    "        nclassifier += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_Survived_RFC = pd.Series(RFC_best.predict(test), name='RFC')\n",
    "test_Survived_SVMC = pd.Series(SVMC_best.predict(test), name='SVC')\n",
    "test_Survived_GBC = pd.Series(GBC_best.predict(test), name='GBC')\n",
    "\n",
    "ensemble_results = pd.concat([test_Survived_RFC, test_Survived_SVMC, test_Survived_GBC], axis=1)\n",
    "g = sns.heatmap(ensemble_results.corr(), annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.2 Ensemble modeling "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.2.1 combining models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "votingC = VotingClassifier(estimators=[('rfc', RFC_best),\n",
    "                                       ('svc', SVMC_best),\n",
    "                                       ('gbc', GBC_best)],\n",
    "                                       voting='soft', n_jobs=-1)\n",
    "votingC = votingC.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "# votingC = GBC_best.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.3 Prediction "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Voyager\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    }
   ],
   "source": [
    "test_Survived = pd.Series(votingC.predict(test), name='Survived')\n",
    "results = pd.concat([IDtest, test_Survived], axis=1)\n",
    "results.to_csv('ensemble_ptyhon_voting_2019_11_11.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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